News & Articles

A report by Guardrail Technologies analyzed how S&P 500 companies document their AI usage alongside their AI cybersecurity measures.

The DOJ and other federal entities were targeted by a Chinese state-sponsored actor.

Claude Opus 4.6 exploited a gym API flaw in 9 of 10 controlled tests, highlighting security risks when AI agents gain backend access. The post Claude Opus 4.6 Found a Gym API Flaw — Then Exploited It in 9 of 10 Tests appeared first on TechRepublic.

Presented by Gravitee Agent complexity is the insidious shadow lurking inside enterprises right now that needs a light shone on it.That’s because enterprises don't deploy a single agent and watch it run, they deploy fleets, each one calling APIs, calling other agents, reaching into applications that were never built with a machine decision-maker in mind. That's the failure mode that should keep you up at night: a windy, complicated system nobody can see clearly enough to govern. But why do things get so opaque so quickly?Add a second agent to a system, and you've added one connection. Add a tenth, and you haven't added ten connections, you've potentially added dozens, because now any agent might call any other, and each of those calls can trigger a call somewhere else. Complexity doesn't creep up with agent headcount. It compounds with the number of paths between agents, and nobody's job is to draw that graph. A support ticket that used to touch one system might now pass through four agents before a human ever lays eyes on it, and every one of those handoffs is a decision point nobody approved.Most enterprise AI programs stall when the humans responsible for their agents lose the thread. Ask a security team a simple question: which agents can reach which systems, and watch the silence. Ask which agent triggered which downstream action three hops ago. More silence.The instinct is to treat this like a checklist. Approve the agent. Log the agent. Move on. I'd argue this is the wrong instinct. A checklist checks a single point in time. Complexity runs across a chain, and you can't govern a chain with a stack of one-time approvals any more than you can call a diet successful because you had a vegetable once.So where does it actually break down?Permissions creep first. Somebody builds an agent to summarize support tickets, grants it broad API access because scoping it properly would've taken another sprint, and forgets about it. Six months later, that same agent has a path into the payments system. Nobody remembers signing off on that. Nobody did.And ownership thins out the further the chain runs. Five agents touch one workflow, something breaks at step four, and now you're asking who's responsible for a link nobody was ever assigned to own, because the org chart stopped at "deploy the agent" and never got to "name the human who answers for it."This is a story about governance infrastructure that hasn't caught up with how agents actually behave: interconnected, cascading, multiplying faster than the processes built to track them.Fixing the cluster starts with identity. Every agent needs to exist as its own entity, not a shadow permission borrowed from whoever deployed it. Its own name in the register. Its own scoped authority. A named human sponsor who answers for what it does. That part is necessary.But it is nowhere near sufficient.The harder piece is the oversight that holds across the entire chain, not just at each individual link in it. You need to see what an agent did, what it set off downstream, and where that trail ends in real time, not in a report someone pulls together once a quarter. Get agent-level identity right and stop there, and you end up with a filing cabinet full of perfectly documented agents operating inside a system nobody can actually explain.And oversight by itself only tells you what already happened. Watching a chain isn't the same as controlling it. Enforcement is the piece most programs skip: the ability to stop an out-of-policy call before it executes, not just log it for someone to find in a review three weeks later. A dashboard that shows you an agent breached its scope five minutes ago is a monitoring tool. A system that stops the breach from happening in the first place is governance. Enterprises serious about agent accountability need both, and most have only built the first.We're all running at blazing speed to ensure we're not the ones left behind in the race we've found ourselves in, and we're all too aware that there's a cost to slowing down. Every enterprise serious about agentic AI hits the complexity wall eventually. The ones that get past it are the ones who built enough visibility and accountability, so their fleet can keep growing without anyone losing the ability to answer one question: what is this system doing right now, and who's responsible for it.But don't miss the point. Complexity isn't a reason to pump the brakes. The enterprises getting this right aren't slowing down. They're building toward Human-Agent Harmony, where scale and accountability grow together instead of trading off against each other.The real risk was never a single agent doing exactly what it was built to do. It's a hundred of them doing exactly that, all at once, interacting in combinations nobody designed for. That kind of multiplication is what keeps enterprise AI stuck running pilots forever instead of running production.Solve for complexity and autonomy stops being the villain. It starts being the whole point.Rory Blundell is CEO at Gravitee. Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.

Ring’s new TAKE encryption limits video-key retention while keeping cloud AI features, Ring Verify, and optional end-to-end encryption in play. The post Ring’s New TAKE Encryption Deletes Video Keys Without Giving Up AI Features appeared first on TechRepublic.

Visa's open-source security harness now finds the vulnerability, writes the fix, and turns an adversarial panel on its own patch before any human reviews it. The whole loop ships on by default. A plain scan of the Visa Vulnerability Agentic Harness runs all 11 stages and edits source files in the target repo unless the operator caps it at detection.The announcement Thursday pairs the release with an expansion of the Visa Consulting & Analytics advisory practice. Visa is shipping that default 18 days after Tenet Security demonstrated GhostJacking on the DEF CON 34 main stage, an attack chain in which an agent read an attacker's payload out of a log file and rewrote DNS with a valid credential. Two days earlier, Steve Wilson, Chief AI and Product Officer at Exabeam and project co-lead for the OWASP Top 10 for LLM Applications, made the case in VentureBeat for the opposite default. "The first thing I'd do is put an authorization gate outside the model," Wilson said in written responses. "The agent can propose the exact DNS change, but it cannot grant itself the authority to make it."The bottleneck moved, so Visa moved the pipelineRajat Taneja, Visa's president of technology, rejects the premise that the default is a risk decision and calls it the product. "The bottleneck has moved," Taneja told VentureBeat in an exclusive interview. "AI is finding vulnerabilities faster than humans can in the history of our technology industry. The new bottleneck is fixing and proving we have fixed things."VVAH grew out of Visa's participation in Anthropic's Project Glasswing, where the company aimed Claude Mythos at the network behind billions of daily transactions and watched the model chain minor weaknesses into working exploits, a hunt VentureBeat covered in July. "VVAH initially was completely only using Mythos, and that's when all of us, as part of Project Glasswing, realized the power of this new class of models that does semantic reasoning," Taneja said.The harness went to GitHub in June and has climbed from 595 stars and 97 forks on July 20 to more than 2,300 stars and 300 forks as of August 25, with a clone-to-visitor ratio Taneja put near 9%. "We have got some very high-profile companies that have started using this harness," he said.Why give it away? Taneja's answer starts with Visa's technology DNA and a harness built "to protect Visa and our ecosystem." The reason he leaned on hardest was obligation, "to do good by doing right" for "companies who may not have the same level of investments or knowledge in cybersecurity."Contribution runs one way. The repo states it is not currently accepting external code contributions, so the harness that edits adopters' source takes no code into its own.Thursday's release extends the pipeline past the report. "We're going from discover, verify, and report, and then fix it, to discover it, verify it, remediate it, validate it, and iterate it," Taneja said. "If a fix doesn't negate the exploit, then there should be a structured, automated feedback that preserves the learnings from the first run and then enhances it." Underneath that loop, the release refactors scanning around an abstract syntax tree call graph that maps subroutine calls and the traversal paths an attacker could reach. Taneja argued the change cuts token counts while delivering "better reasoning, context, and better exploitability analysis." On top sits MTTA observability across the stages, what he called a window pane, plus real-time progress views. "A pretty good step function," he said of the release.One metric, three definitionsMean Time to Adapt, the metric Visa invented alongside the harness, gets a shorter definition in this release. The short form is the time between discovery and resolution of attack paths, with some resolutions, Visa claims, shrinking from weeks to hours. Visa published a wider construction in June, and the Project Glasswing white paper tracks MTTA along three dimensions that include inventory freshness, exploitable paths per release, and validation cycle time. The repo carries a third, elapsed time from AI-discovered exploitability to a validated fix in production. Board slides will quote the shortest interval. Ask for all three, because a resolution count that skips validation is what MTTA was invented to replace.Taneja ranks MTTA as "the most strategically important metric" because it shifts the focus from scanning to how fast an enterprise adapts. His shorthand is blunter. "It's not the finding. It's the fixing that matters," he said.The default and the gateWilson's argument went past naming the gate. "We have to remember that security rules written inside prompts may shape the model's behavior, but they are still suggestions to the model, not enforceable security controls," he wrote. He also priced the control honestly. "The tradeoff is that the agent loses the ability to improvise arbitrary, high-impact infrastructure changes on its own, while retaining autonomous investigation and routine, bounded remediation," Wilson said.The harness ships no approval step between patch and edited file. Where the human sits was the first question VentureBeat put to Visa in writing.The company's own June white paper sets the bar. "AI agents are identities" sits among its 12 non-negotiable practices, requiring scoped permissions, least privilege, audit trails, and IAM governance for every agent that modifies a system. VVAH's shipped default is that agent."A lot of the traditional systems that are used today are basically signal providers," Taneja told VentureBeat. "They are telemetry, and then it's a lot of human analysis, and your SOC and your security and incident response teams doing a lot of the heavy lifting when they respond," and that, he said, cannot work at this scale. He pointed to the Hugging Face incident and "other frontier models escaping sandboxes to do things more autonomously" as the preview. "We have seen the trailer of this movie," Taneja said, and "every company in the world should prepare and rethink their architecture."What the harness automates is the adversarial step. Before a fix counts as validated, the panel scores whether the patch negates the exploit, Taneja's test for done, with failed fixes feeding the next attempt, the iterate step Taneja described. Stage 11 itself runs read-only, per the README, and VVAH does not compile, build, or run tests against the patched tree. Taneja calls that wrapper "the governance architecture on top of that," and chaining findings into working exploits takes threat modeling and business context, which is why he argued "the harness with a model is far more effective than somebody using the model by itself."Visa answers the gate questionVentureBeat put its questions to Visa in writing after the interview, and the answers arrived before publication. On why remediation ships on, the response repeated the bottleneck argument, then narrowed the scope. "VVAH is meant for authorized operators running against code they own, and in a controlled environment," the company said in written responses.The approval question drew the most specific answer. "VVAH is a harness, not a merge tool," Visa wrote. "Stage 10 writes candidate fixes to a working copy of the repo. Stage 11 then runs an adversarial validation panel that scores each fix and returns one of three verdicts: validated, validation failed or needs review. None of these bypasses your normal build, test, and code review flow." Humans, the company wrote, are "the gate in three places. Before running the tool. When reviewing the patches. And before anything gets merged." "The final call on any fix stays with the security and engineering teams. In an enterprise, trust and auditability are not optional. The default flow is built around that."Set beside Wilson's standard, the architecture lands close to his line and the sequence does not. Wilson's gate clears an action before it happens. The default's human gates open before the run and after the write. The attack is the automated part, and the three human gates sit outside the model, the boundary Wilson drew. "Our goal is to help security teams work at AI speed, not to replace them," Visa wrote. "VVAH does the repetitive parts. It finds issues, tests whether they are real, and proposes fixes. Before a fix gets to a human, an adversarial validation panel at stage eleven tries to break it. That way the human is spending time on decisions that need judgment, not on triaging noise."Client zero got direct confirmation. "VVAH runs against Visa code today," Visa wrote, and Taneja had volunteered the posture on the call. "We designed this and we were using it for ourselves, and we were client zero," he said, adding "only when we saw the impact and the positive effect of what we were finding, we said every company would need this." What adopters value, Visa says, is context. VVAH pulls in CMDB data, threat models, and business risk, and where "most tools stop at findings," it "tries to answer, 'which of these should you fix first, given how your business runs.'"Model choice becomes a per-stage decisionMulti-model orchestration is the other substantive change. "Mythos has a very high recall, but the Opus model has very high precision," Taneja said. "On stage one I want to use this model. On stage two I want to use this model," is how Taneja framed the per-stage setup, with newer GPT releases in the ensemble and open-weight models where pricing stings, all through configuration rather than code changes. "The whole is greater than the sum of the parts," as he put it. The harness was model-agnostic from day one, he added, and the evolution moved that choice into configuration, with prompt tuning and caching shared underneath. One boundary moved. In June, applying a fix required Anthropic backends, and OpenAI-compatible backends ran report-only. The current README extends remediation and validation to OpenAI-compatible and open-weight models through a shared model-agnostic runtime, with no single provider as a hard dependency, and the default routing for both stages stays Anthropic.That flexibility lands on a market already churning. VentureBeat's Q2 2026 Pulse research found 59% of enterprises plan to adopt or switch agent security tooling within the year, and 82% still rely on provider-native controls as the primary layer. Visa said Thursday it is contributing VVAH to Nvidia's Open Secure AI Alliance as a model-agnostic framework and collaborating in Project Lightwell, the $5 billion IBM and Red Hat effort to harden open-source components.Before turning fix mode onDecisionWhat to establish firstRun postureStart with --stop-after s9 and read the SARIF output before any run that can write to source files.Approval gateMap Visa's three human gates onto the pipeline, at run, at patch review, and at merge, and name who holds each.Validation scopeStage 11 verdicts score the fix. Build, test, and code review stay in the team's own flow, per Visa, so keep an exploit re-test before merge.Repository scopeThe tool runs with elevated privilege, per its own README. Fence which repos the harness can reach, and run scans in an ephemeral environment with scoped credentials, no production secrets, and network limited to the target repo and model endpoint. Write access to production code is the GhostJacking exposure class, an agent acting on data it read. Per the README's egress warning, any role routed through the SDK, OpenAI, or DeepAgents backends sends prompt data to that provider's endpoint.Model rolesAssign models per stage deliberately. Recall and precision differ by model, per Taneja, the fix stages carry the highest blast radius, and the README states precision and recall figures are not yet published, so measure your own.Consulting is the other half of Thursday's announcement. Visa Consulting & Analytics is adding executive workshops, a VVAH-informed maturity assessment scored on a NIST one-to-five scale, and a cyber risk prioritization roadmap. "We were getting a lot of calls. Hey, can you help?" Taneja said, and the practice "became very important to handhold and help those who are using it." Carl Rutstein, global head of Visa Consulting & Analytics, framed it the same way. "Finding vulnerabilities is no longer the hardest part. Speed to remediation is the new battleground."

More than 50 gigabytes of documents were reportedly stolen.

Presented by EDB As enterprises give AI agents more autonomy — the ability to plan, decide, and act across systems without a human approving each step — a hard question moves to the center of every architecture review: When an agent tries to complete an action that it was never authorized to do, what actually stops it?These are your agents, running on your models, touching your data in your infrastructure — and the responsibility for what they do sits with you. That responsibility can’t be met in hindsight or with a set of abstract policies that live on paper but not in practice. Agents need rules in the context of the moment, because they don’t exercise overriding judgment of their own actions.Consider a simple rule: Never open the car door. Followed literally, an agent could never get in or out of the car at all. But if you change the context (the car has just crashed, there’s a fire, someone is hurt and needs to get out), then the rule you actually want is the opposite. Context in the moment is everything. We are asking agents to do intelligent things; that requires intelligent rules.The instinct is to add guardrails around the agent: instructions, policies, and monitoring layered above the model. Those mechanisms matter, but they share a structural limit: The car-door rule is plausible right up until the moment you actually have to decide whether to open the door. Controls at the agent layer are only as reliable as the agent’s output is predictable, and autonomy is precisely the property that makes that output hard to predict. Governance that depends on reviewing an action before it happens cannot keep pace with a system that acts in milliseconds, across many systems at once.Governance has to become executable, and enforced where agents actually do their work: at the operational data layer, in the context, and exactly at the moment it is happening. The data layer is the enforcement pointAgents create value by touching data. They query it, retrieve it, transform it, and increasingly act on it. A policy that says an agent should not reach a certain class of data is meaningful only if the system can deny that access at the moment the agent requests it. Additionally, a principle that says AI must be auditable is meaningful only if the organization can reconstruct what the agent did, what data it touched, which user it acted for, and what resulted. When governance lives at the data layer, it holds regardless of how the agent was built or how it behaves, because the control is a property of the database itself, not a promise made by the agent.Agent behavior may be probabilistic. Governance cannot beThe enterprise should not rely on a model choosing to follow policy. The policy has to be enforced by the system. That is the difference between hoping an actor stays in bounds and constructing bounds it cannot cross to begin with.The controls that make this real are ones many enterprises already run at the data layer: role- and attribute-based access, row- and column-level security, classification and masking, policy as code, and complete audit trails. What agents change is not the mechanism, but who the mechanism has to recognize. Identity management has to treat the agent as a principal in its own right, with its own identity and a purpose declared when the session opens. Once purpose is bound to identity, the policy engine can evaluate it the same way it evaluates role or department today, and the record of what happened can capture not just who acted and what they touched, but what they declared they were there to do.In practice, this resolves into nine controls, grouped under three imperatives:Enforce itRole- and attribute-based access control enforced at query time, for agents as well as usersDynamic column masking driven by the same policy pathAgent identity as a first-class principal, with declared purpose bound at session start and the acting user preservedSee it and prove itClassification and tagging that drives policySession-level audit logging that records which agent acted, for which user, and under what declared purposeLineage across pipelines, so a result can be traced back to the request that produced itUnify and hardenCentralized, portable policy managementEncryption at rest and in transitConsistent enforcement across on-prem, cloud, and sovereign or air-gapped environments“Declared purpose is what makes the difference. It becomes an attribute the access layer already understands, evaluated in the same policy path as role and row-level security. The enforcement mechanism does not change. What changes is that the agent's purpose is part of what it evaluates, and part of what the record proves afterward,” says Priyanka Jain, VP, product management, data & AI governance, EDB. Wherever you are in your AI adoption journey, enforcement at the data layer is what lets you move faster rather than slower. The controls are already in the database. The difference is that agents now have to pass through them.A digital leash, not a locked doorThe goal is not to stop agents from doing useful work. It is to define how far an agent can go, what it can touch, what it can change, what requires escalation, and how the organization can reconstruct events if something goes wrong. Governed this way, agents are identified, scoped, monitored, and auditable. The enterprise can adopt them faster, because security, risk, and leadership teams trust the operating model underneath.Open, sovereign, and enforceable at the sourceBuilt on open source Postgres, this open foundation keeps enterprises in control of where their data lives, who can reach it, and under what policy, without ceding governance to a layer they don’t own or can’t inspect. For regulated industries, that combination of data sovereignty and source-level enforcement isn’t a nice-to-have; it’s the precondition for putting agents into production at all.Agentic systems will keep getting more capable and more autonomous. That is a reason to be deliberate about where control lives, not a reason to slow down. The enterprises that enforce governance at the data layer can move aggressively on AI, because the thing protecting their data is more than just wishful thinking. EDB Postgres AI is an open, enterprise-grade sovereign data and AI platform that unifies transactional, analytical, and AI workloads — with governance enforced where the data lives. For the full framework, see EDB’s white paper Governing Agentic AI at Enterprise Speed.Max Romanenko is Chief Technology Officer at EDB.Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.

Presented by EDB As enterprises give AI agents more autonomy — the ability to plan, decide, and act across systems without a human approving each step — a hard question moves to the center of every architecture review: When an agent tries to complete an action that it was never authorized to do, what actually stops it?These are your agents, running on your models, touching your data in your infrastructure — and the responsibility for what they do sits with you. That responsibility can’t be met in hindsight or with a set of abstract policies that live on paper but not in practice. Agents need rules in the context of the moment, because they don’t exercise overriding judgment of their own actions.Consider a simple rule: Never open the car door. Followed literally, an agent could never get in or out of the car at all. But if you change the context (the car has just crashed, there’s a fire, someone is hurt and needs to get out), then the rule you actually want is the opposite. Context in the moment is everything. We are asking agents to do intelligent things; that requires intelligent rules.The instinct is to add guardrails around the agent: instructions, policies, and monitoring layered above the model. Those mechanisms matter, but they share a structural limit: The car-door rule is plausible right up until the moment you actually have to decide whether to open the door. Controls at the agent layer are only as reliable as the agent’s output is predictable, and autonomy is precisely the property that makes that output hard to predict. Governance that depends on reviewing an action before it happens cannot keep pace with a system that acts in milliseconds, across many systems at once.Governance has to become executable, and enforced where agents actually do their work: at the operational data layer, in the context, and exactly at the moment it is happening. The data layer is the enforcement pointAgents create value by touching data. They query it, retrieve it, transform it, and increasingly act on it. A policy that says an agent should not reach a certain class of data is meaningful only if the system can deny that access at the moment the agent requests it. Additionally, a principle that says AI must be auditable is meaningful only if the organization can reconstruct what the agent did, what data it touched, which user it acted for, and what resulted. When governance lives at the data layer, it holds regardless of how the agent was built or how it behaves, because the control is a property of the database itself, not a promise made by the agent.Agent behavior may be probabilistic. Governance cannot beThe enterprise should not rely on a model choosing to follow policy. The policy has to be enforced by the system. That is the difference between hoping an actor stays in bounds and constructing bounds it cannot cross to begin with.The controls that make this real are ones many enterprises already run at the data layer: role- and attribute-based access, row- and column-level security, classification and masking, policy as code, and complete audit trails. What agents change is not the mechanism, but who the mechanism has to recognize. Identity management has to treat the agent as a principal in its own right, with its own identity and a purpose declared when the session opens. Once purpose is bound to identity, the policy engine can evaluate it the same way it evaluates role or department today, and the record of what happened can capture not just who acted and what they touched, but what they declared they were there to do.In practice, this resolves into nine controls, grouped under three imperatives:Enforce itRole- and attribute-based access control enforced at query time, for agents as well as usersDynamic column masking driven by the same policy pathAgent identity as a first-class principal, with declared purpose bound at session start and the acting user preservedSee it and prove itClassification and tagging that drives policySession-level audit logging that records which agent acted, for which user, and under what declared purposeLineage across pipelines, so a result can be traced back to the request that produced itUnify and hardenCentralized, portable policy managementEncryption at rest and in transitConsistent enforcement across on-prem, cloud, and sovereign or air-gapped environments“Declared purpose is what makes the difference. It becomes an attribute the access layer already understands, evaluated in the same policy path as role and row-level security. The enforcement mechanism does not change. What changes is that the agent's purpose is part of what it evaluates, and part of what the record proves afterward,” says Priyanka Jain, VP, product management, data & AI governance, EDB. Wherever you are in your AI adoption journey, enforcement at the data layer is what lets you move faster rather than slower. The controls are already in the database. The difference is that agents now have to pass through them.A digital leash, not a locked doorThe goal is not to stop agents from doing useful work. It is to define how far an agent can go, what it can touch, what it can change, what requires escalation, and how the organization can reconstruct events if something goes wrong. Governed this way, agents are identified, scoped, monitored, and auditable. The enterprise can adopt them faster, because security, risk, and leadership teams trust the operating model underneath.Open, sovereign, and enforceable at the sourceBuilt on open source Postgres, this open foundation keeps enterprises in control of where their data lives, who can reach it, and under what policy, without ceding governance to a layer they don’t own or can’t inspect. For regulated industries, that combination of data sovereignty and source-level enforcement isn’t a nice-to-have; it’s the precondition for putting agents into production at all.Agentic systems will keep getting more capable and more autonomous. That is a reason to be deliberate about where control lives, not a reason to slow down. The enterprises that enforce governance at the data layer can move aggressively on AI, because the thing protecting their data is more than just wishful thinking. EDB Postgres AI is an open, enterprise-grade sovereign data and AI platform that unifies transactional, analytical, and AI workloads — with governance enforced where the data lives. For the full framework, see EDB’s white paper Governing Agentic AI at Enterprise Speed.Max Romanenko is Chief Technology Officer at EDB.Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.

Boston Scientific, a U.S. medical device manufacturer, announced a cyberattack affecting their global operations.

A report by SentinelOne and Tenable Holdings analyzed vulnerability discovery, exposure, and exploitation.

There is no legitimate GTA 6 demo. Fake Rockstar sites are distributing Vidar malware that targets passwords, cookies, and logged-in browser sessions. The post Fake GTA 6 Demo Spreads Malware: How to Spot the Scam appeared first on TechRepublic.

A smart monitor can do far more than display a PC. Built-in apps, ACR, and advertising systems can introduce additional privacy considerations. The post Do Smart Monitors Track You? What Buyers Should Know appeared first on TechRepublic.

OpenAI banned Russian ChatGPT accounts tied to a covert influence campaign involving copied research, fake attribution and coordinated social posts. The post OpenAI Bans Russian ChatGPT Accounts Used in Covert Influence Campaign appeared first on TechRepublic.

More than 100 water systems were targeted with cyberattacks. Security leaders weigh in.

Research examined artifacts from the campaign and discovered hundreds of organizations targeted.

A security agent read a Cloudflare log, found an attacker’s prompt-injection payload sitting inside it, and rewrote the company’s DNS. The firewall had already blocked that payload, and blocking it is what wrote it into the log.That chain is GhostJacking, which Tenet Security demonstrated on the DEF CON 34 main stage on August 9. A request hits Cloudflare’s managed ruleset, gets blocked, and is stored byte for byte with its poisoned User-Agent header. An AI coding agent reviewing those blocked events reads the attacker's text as an instruction — with no way to tell it apart from one the company meant to give it — and acts on it with credentials the company issued months earlier. In Tenet’s benchmark, Claude Code on Sonnet 4.6 followed the planted instruction in nine of 10 attempts under Cloudflare’s recommended configuration.The block rate is not the boundaryNothing malfunctioned. The firewall worked, and every call after it carried a valid credential already issued to the agent. Endpoint detection, the web application firewall and identity management stayed quiet because no rule broke.Tenet found public evidence of the exposed setup at 48 organizations, six confirmed Fortune 500 companies, and SecurityWeek reported the same chain against Datadog and Sentry, where the injection surface is an alert or an error report. No single platform patch removes the architectural risk: an agent that consumes attacker-reachable data and can independently execute high-impact changes. That is why a high prompt-injection block rate cannot serve as a security boundary.OWASP’s co-lead names the fix“The first thing I’d do is put an authorization gate outside the model,” said Steve Wilson, Chief AI and Product Officer at Exabeam and project co-lead for the OWASP Top 10 for LLM Applications, in written responses to VentureBeat. “The agent can propose the exact DNS change, but it cannot grant itself the authority to make it.”The move relocates the decision into code that either passes or fails. A safe change, defined cleanly, clears a deterministic policy check and stays autonomous. Anything ambiguous or high in blast radius routes to a named human who approves the actual change.“The tradeoff is that the agent loses the ability to improvise arbitrary, high-impact infrastructure changes on its own, while retaining autonomous investigation and routine, bounded remediation,” Wilson wrote. On teams that try to solve this inside the prompt, Wilson is blunt. “We have to remember that security rules written inside prompts may shape the model’s behavior, but they are still suggestions to the model, not enforceable security controls,” he wrote.The blocked payload became the instructionGhostJacking needs no compromised admin account and no bypassed firewall. It needs an agent that reads operational data and holds write access to the systems that data describes. Every step in Tenet’s chain is something the agent was already allowed to do, so tools tuned to catch unauthorized actions have nothing to catch.The mechanism is specific. SC Media reported that the agent Tenet drove live at Cloudflare was Cursor, reading through a GraphQL integration and writing through the Cloudflare API, and that pairing closes the chain. Tenet ran the chain against more than one coding agent. Cursor carried the demonstration, and the nine of 10 figure came from separate testing of the same attack against Claude Code. The Cursor agent ingests the poisoned header, patches the DNS A record and adds a CNAME to “resolve” the injected finding, giving the attacker a path to reroute the company’s web and email traffic.One agent’s output became the next agent’s inputEvents reach Sentry through a public write-only endpoint with no authentication, by design. Tenet used a leaked identifier to post a crafted error report. On an ordinary triage prompt, the coding agent escalated it to Sentry’s own AI, Seer, then trusted the analysis it got back. Seer had already absorbed the attacker’s proposed fix and returned it as its own finding. What reached the coding agent was a recommendation from another AI, and it implemented that recommendation.That walked straight through a control Sentry had already written. Sentry's guidance instructs agents reading its event data never to follow directives found there, and the coding agent held to the letter of that rule. It acted on Seer's conclusion instead, and that conclusion belonged to the attacker. It was acting on Seer’s conclusion, and that conclusion belonged to the attacker. An authorization boundary that accepts another model’s output inherits every injection that model absorbed, which is why Wilson’s gate belongs between agents too.OWASP moved excessive agency from sixth to thirdThe 2026 OWASP Top 10 for LLM Applications, published August 4, elevated Excessive Agency three places on a ranking blending a 75% practitioner vote with 25% incident data from 6,639 documented cases. It was the largest upward move on the list, driven by real-world incidents clustering in agentic deployments.The fix isn't better prompting — it's the permission map: which actions are pre-approved, and which need a human. Reading logs, correlating alerts and drafting timelines stay autonomous. A bounded remediation like restarting a named service inside a fixed condition set clears a policy check outside the model. Anything that changes DNS, alters identity privileges, deploys code or reroutes production traffic needs a named human, and letting an agent open new access paths or approve its own proposals defeats the point of the gate. Useful autonomy survives that. What does not survive is the path from an attacker’s text to unreviewed production authority.What the control costs in practiceTenet co-founder and CEO Barak Sternberg told Dark Reading that a request the firewall already blocked was the way in, and that the firewall never went down, it just stopped mattering. His own fix is to split what an agent can read from what it can execute, and he concedes the cost, because an agent that reads alerts but cannot act on them is not the agent anyone deployed. The cheaper first step is an inventory. Every agent that reads outside data and can also write or execute belongs on a risk register, and that register needs no new tooling.Wilson’s design survives that cost because the split it draws is proposal from approval rather than read from write. The agent still reads its alerts, still investigates and still runs bounded work. What it loses is the ability to invent a high-impact change and carry it out on its own authority.Almost nobody has built it. Kayne McGladrey, a senior member of the IEEE, has argued for years that an AI deployment needs a hard governance threshold, a named human holding a kill switch and a way to roll back. Asked whether any Fortune 500 company runs that, he was blunt. “I haven’t seen it done, and no, they haven’t come out and publicly said it,” McGladrey told VentureBeat.His reason is economic. Companies “are accepting the risk, and they’re accepting it either deliberately or unconsciously,” he observed, betting the advantage beats the penalty. “What I think would change behavior in the space is if the penalties and the consequences were to outweigh the advantages.” The gate belongs outside the model for a reason unrelated to malice. “If you get into the inference layer, it won’t tell you it’s cheating, and it will lie about having cheated,” he argued, pointing to findings from the U.K.’s AI Safety Institute among others. A system that cannot reliably report its own shortcuts should not authorize its own.The industry is not positioned to make that split quickly. Ivanti’s 2026 State of Cybersecurity Report found 77% of security professionals at least somewhat comfortable letting AI act without human review, the exact posture Wilson’s gate constrains. CrowdStrike pushed its prompt-injection taxonomy past 200 techniques in July, naming indirect injection through data an agent reads as the critical vector for agents that call tools and run commands.The architect who moved the boundary before the attack had a nameEgiziago Cioffi hit a related failure in production months before GhostJacking had a name, with one caveat. Cioffi is CEO of SynSphere Italia, a Microsoft reseller, the architect who built and sold the system rather than a security leader defending one he inherited. His Azure OpenAI assistant over SharePoint scored well on faithfulness and still returned content the asking user could not have opened. “An evaluation set with no identity dimension cannot fail an authorisation bug, however high the faithfulness score,” Cioffi told VentureBeat in written answers. He fixed it with a query-time filter built from the asking user’s group claims, so an unentitled chunk never becomes a candidate and never reaches the model. GhostJacking turns on what the model may do, the half Wilson’s gate is built for.One gap stays open here. No sitting CISO has gone on the record with a change made since August 9 and what it cost in agent capability. Until one does, the cost side rests on the people who specified the control, not anyone defending a production environment with it.What security leaders need to do this weekFour questions produce an honest picture faster than any procurement cycle. Which agents read attacker-reachable material, which of those can also change production systems, whose permissions run at retrieval, and which changes a policy engine can approve without a human.Then run the negative test. Plant an adversarial instruction in a log the agent is expected to inspect, and keep the transcript, because that transcript is the difference between claiming a control and showing a test of it. Tenet, which sells runtime protection for AI agents, leads its own defender guidance with denying an agent outbound network access by default, cutting the leg where the poisoned instruction fetches a payload and reroutes traffic. But an agent that investigates without a standing path to the open internet loses a capability few workflows will miss.Enumerate the service principals in the tenant, drop the Microsoft first-party apps that ship pre-provisioned, then filter to those holding a credential or app-role assignment. Every identity on that list needs an owner and an expiry date, because one with both gets reviewed and one with neither never does. For any agent with production authority, write the containment sequence before an incident, not during one. Revoke or rotate its workload credential, disable its write-capable API or tool integration, preserve the execution transcript, then validate and roll back whatever infrastructure it changed.McGladrey’s read on why the work keeps getting deferred is uncomfortable. “I think that there’s a level of tolerance that’s being given right now in AI that is unlike anything else in society,” he said. GhostJacking makes that visible. A blocked payload reaches the agent through the system built to record blocked payloads, and once it arrives the question is no longer whether the model recognizes an attack. It is whether the model holds the authority to turn one into a production change.

Red team tests were conducted at the behest of two critical infrastructure organizations.

Presented by Tata Communications Enterprises are deploying AI agents, voice AI, and automation across messaging, voice, and digital channels faster than the architecture meant to support it. Most of that deployment has involved attaching conversational AI to legacy systems never built for it, says Gaurav Anand, global head of the Customer Interaction Suite at Tata Communications."In the rush to deploy AI, organizations have largely bolted conversational AI onto legacy systems," Anand says. "As a result, while many enterprises have adopted digital tools, very few have platforms that are truly integrated, scaled, and capable of seamless orchestration."That gap creates a heavy cognitive load for human agents who must piece together context across disjointed tools to understand what an AI system has already told a customer. The challenge is not simply access to data, but the absence of a shared enterprise context that connects customer identities, interactions, transactions, policies, journeys, and operational systems into a common understanding. Traditional CX architecture was built for linear, human-driven routing, not for managing real-time data flows between autonomous AI systems, data lakes, and human workers."Today's operational complexity is no longer about adding more intelligence," he adds. "It is about coordinating the existing intelligence across the enterprise, so the enterprise customer never feels the friction of those internal silos. That requires a shared context layer that allows AI systems, applications, and people to operate from the same understanding of the customer and the business."Why orchestration is replacing automation as the top CX priorityAs that coordination problem grows, Anand says the strategic priority inside enterprises is shifting from automation to orchestration."Automation solves individual tasks, whereas orchestration connects them into end-to-end outcomes," Anand says. "The next evolution is context-aware orchestration, where AI agents, applications, and human workers operate using a shared understanding of customers, processes, and business intent rather than isolated system records."As organizations accumulate more bots, agents, and AI tools, managing them grows exponentially more complex. Anand says the competitive advantage now sits less in deploying automation and more in how intelligently systems hand off work, collaborate, and escalate. The trap of bolting AI onto legacy systemsCompanies that simply place a voice AI agent in front of an existing system are repeating the same old mistake. Instead of improving the experience, they end up recreating the deterministic phone menus AI was supposed to replace. The real benefit of AI is the scale, speed, and orchestration it provides.Anand points to a wave of consolidation across the industry, as established contact center providers acquire AI-native firms to close capability gaps and strengthen their customer experience offerings. The broader industry shift reflects a growing recognition that enterprises need more than channels and automation; they need an intelligence layer capable of orchestrating AI, people, data, and workflows across the business.The goal across industries is to make AI the connective layer between customers, employees, and enterprise systems. To achieve that, organizations increasingly need a common enterprise ontology: a shared business vocabulary that aligns customer data, products, policies, SOPs, transactions, and workflows across otherwise disconnected platforms.Tata Communications’ solution is the Interaction Fabric, an orchestration layer that unifies contact center, messaging, collaboration, AI, and customer data while coordinating AI agents, channels, and enterprise systems in real time. Underpinning that orchestration is a context-driven architecture that continuously connects identities, conversations, transactions, and operational data so interactions retain continuity across channels and touchpoints.That means AI and agents can move across voice, WhatsApp, chat, email, and CRM workflows without losing customer context. Identity, intent, and AI-driven insight flow continuously across channels instead of remaining trapped in disconnected applications.The next phase of orchestration is not simply coordinating tasks across systems, but coordinating them through a shared understanding of the enterprise. Context graphs, built on enterprise ontologies, create that common understanding by connecting customers, interactions, products, policies, decisions, and outcomes across organizational silos. This allows AI agents and human workers to operate from the same source of context, driving more accurate decisions, seamless handoffs, and consistent customer experiences.But synchronizing customer intent, conversation history, enterprise data, and AI decision-making across channels only works without lag. Legacy networks not designed for modern data frequency create what Anand calls data gravity, producing latency and inconsistent journeys as users switch channels."The underlying network needs to be engineered to be as agile as the AI systems running on top of it," he explains. "Interactions stay synchronous and technology itself becomes invisible, leaving only an experience that feels effortless."Making AI a better partner for human agentsEffective shared visibility between human agents and AI systems starts with the agent experience rather than any single technology. The most effective implementations allow both the AI and human agent to operate from the same contextual understanding of the customer, ensuring that information gathered in one interaction can inform the next regardless of channel or system. Automated call summaries, real-time sentiment analysis, and AI-powered assistance provide agents with instant, actionable insights and suggested next steps directly within their workflow.That allows AI to handle routine, high-volume tasks such as password resets, delivery tracking, and account updates, while human agents focus on interactions requiring judgment and empathy."If a customer is facing a sudden crisis like a fraudulent transaction, the AI can instantly block the card, but it cannot provide the emotional comfort and delicate communication needed in that moment of panic," Anand says. "The answer to the dilemma is intelligent orchestration, rather than a choice between systems." In practice, AI handles the immediate technical transaction, while real-time sentiment analysis recognizes the customer's distress and routes the call to a human expert. The objective is to orchestrate AI and human agents together so efficiency never comes at the cost of brand trust and loyalty.Building a unified CX architectureMoving from fragmented experimentation to coordinated orchestration requires both technical and organizational change, Anand says, beginning with consolidating data and fragmented point solutions onto a unified, cloud-first platform."IT and CX teams need to work more collaboratively," he explains, describing that alignment as the second necessary shift, this time at the organizational level.At the architecture level, Anand says communication APIs need to be embedded into the enterprise's core so every function operates from the same customer context instead of maintaining its own siloed data. Increasingly, this means moving beyond integration alone toward a contextual architecture where a shared ontology and context graph provide a common understanding across CX, operations, sales, service, and AI systems. The deeper organizational change, he says, is a mindset shift from reactive support toward proactive, predictive, and personalized engagement, which he calls the three Ps.How AI agents will shape the future of CXCustomer engagement over the next several years will be defined by real-time intelligence, increasing autonomy, and seamless orchestration across touchpoints, and persistent enterprise context that follows customers, employees, and AI agents wherever interactions occur. Rather than analyzing interactions after the fact, enterprises will increasingly shape conversations in real time. "The future of CX will be defined by simplification, aligning data, infrastructure, and operating models around clear customer outcomes rather than adding more models and tools," Anand says. "The rise of AI-powered agents and agent-to-agent interactions is a defining trend, with AI systems moving beyond assisting humans to independently managing and resolving interactions, creating a largely invisible layer of engagement that improves speed and efficiency." Human agents will increasingly work alongside AI, supported by real-time conversational intelligence and next-best-action recommendations to deliver what Anand calls Total Experience: a unified model that brings together customer, employee, and AI-driven experiences. Tata Communications is building toward that future through its Voice AI, AI Workers, and Total Experience Hub solutions."Ultimately, customer engagement will evolve from being reactive to predictive and increasingly generative," Anand says. "Enterprises won't just be responding to needs, but actively shaping and improving customer journeys in real time."Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.

WhatsApp is adding stronger two-step verification, multiple passkeys and more context for unknown callers as it expands protections against scams. The post WhatsApp Just Added 3 New Ways to Protect Your Account appeared first on TechRepublic.

Password notebooks are making an unexpected comeback as infostealers and browser attacks revive debate over the safest way to store credentials. The post The Password Notebook Is Back — but Is It Actually Safer? appeared first on TechRepublic.

TikTok and ByteDance agree to pay up to $400 million to settle US allegations involving children’s data, parental consent and account deletion. The post TikTok Agrees to $400M Settlement Over Children’s Privacy appeared first on TechRepublic.

A CISO who sees a low CVE count and deprioritizes prompt injection is reading the scoreboard wrong. Prompt injection has held the No. 1 spot on the OWASP Top 10 for LLM Applications for three consecutive years. When two leaders of that list checked it against 6,639 labeled real-world incidents, it came back at No. 12. The drop measures visibility rather than danger, because the attack operates where a vulnerability scanner cannot see it.That finding belongs to Kyriakos “Rock” Lambros and Steve Wilson, two leaders of the OWASP Top 10 for LLM Applications project, who published it on arXiv on August 18 with the disclaimer attached. The analysis is exploratory, not peer reviewed, and not the official OWASP release, and the authors state it does not supersede the official list or its process.The machinery behind it is real: 7,714 LLM security incidents from CVE, GitHub Security Advisories, OSV, and the AIAAIC AI-harm database, 6,639 of them labeled against a 20-entry taxonomy, and a Bayesian model that corrects each count for classifier error before setting the data-driven ranking beside the expert vote.The comparison found no statistically detectable agreement between expert judgment and the public incident record. Cohen’s kappa comes in at 0.20 with a 90% interval running from negative 0.16 to 0.57. “The interval crosses zero, so we cannot rule out that the two rankings agree only by chance,” they write. “The honest bottom line: weak agreement, not confirmation.”Lambros, co-lead of the OWASP GenAI Security Project Top 10 for LLM Applications and director of AI standards and governance at Zenity, put the finding in evidentiary terms in written answers to VentureBeat. “We had two ways of measuring the same risk, expert judgment and the public incident record, and they disagree with each other. Neither one is the truth,” Lambros said. “Two witnesses are contradicting each other, and we can’t tell you which one is lying.”The attack chain a scanner never logs The gap is structural. Prompt injection hides instructions inside the content a model reads, anything from a log entry to a support ticket to a document pulled back by retrieval. The agent then makes the tool call the attacker wanted, using credentials it legitimately holds. Nothing in that chain is a product defect, so the attack leaves no CVE behind for a scanner to find.The defenses that catch it are adversarial tests against the deployed system and hard caps on what the agent can reach, so a fooled model cannot touch anything expensive. The same logic argues for funding agent memory and MCP tool boundaries now, on architecture, rather than waiting for advisory volume that will always arrive a cycle late.The first control Wilson would deploy Wilson, Chief AI and Product Officer at Exabeam and project co-lead for the OWASP Top 10 for LLM Applications, named the control he would deploy first against exactly that chain, an agent that reads an attacker’s payload in a log file, treats it as an instruction, and rewrites DNS with a valid credential, in written responses to VentureBeat.“The first thing I’d do is put an authorization gate outside the model: the agent can propose the exact DNS change, but it cannot grant itself the authority to make it,” Wilson said. “Security rules written inside prompts may shape the model’s behavior, but they are still suggestions to the model, not enforceable security controls.”The gate has a price, and Wilson states it plainly. “The tradeoff is that the agent loses the ability to improvise arbitrary, high-impact infrastructure changes on its own, while retaining autonomous investigation and routine, bounded remediation,” he said.Why the No. 1 risk looks small in the record “Prompt injection is the best-understood LLM attack, and deployed systems defend against it actively,” the authors write, and they compress the whole divergence into one sentence. “Experts rank it first because the attack surface stays enormous even when the defenses mostly hold; the data sees the successes that got through.”Wilson has watched the gap from both sides of it. “Incident data is incredibly valuable, but it is inherently backward-looking and notoriously tricky to interpret,” he said. “It tells us what was observed, recognized, classified, and reported. It does not necessarily tell us what is most dangerous in the systems people are building right now.”He compares prompt injection to “death and taxes” and, increasingly, to “a law of physics for LLM systems,” because one model is being asked to interpret trusted instructions and untrusted content at the same time.Better defenses have not closed the case. “A control that works 99% of the time is not sufficient when the failure case gives an attacker meaningful access. And, frankly, I don’t think we are at 99%,” Wilson said. “The durable answer is not believing we can perfectly screen prompt injection out of existence. It is designing systems with the assumption that prompt injection will occur, understanding why it works, and limiting what an attacker can accomplish when it does.”A low advisory count can mean the defenses are working. It can just as easily mean nobody has looked, and the public record cannot tell a security team which one it is.The attempt volume is documented. CrowdStrike’s 2026 Global Threat Report found adversaries injected malicious prompts into legitimate GenAI tools at more than 90 organizations in 2025, stealing credentials and cryptocurrency, under a section titled “Prompts are the New Malware.” The telemetry shows pressure on the attack surface without proving defenses produced the No. 12 placement, but it is the pattern the mechanism predicts.The gap runs the other way too, and further Prompt injection is the headline case, and misinformation is the bigger one.The expert vote puts misinformation at No. 13, while the incident record places it at No. 2. The paper calls it “the widest disagreement between the two witnesses” and reports that its concordance flag “puts the probability that the two signals disagree at 99 percent.”The authors do not treat their own data as the winner. On misinformation they note the corpus “carries a large volume of deepfake and AI-generated disinformation,” records that often “describe harm produced by an AI rather than a vulnerability inside an LLM.” The authors call it the entry the record most disputes, stopping short of concluding the experts got it wrong.Where “too new to measure” runs into the CVE record The two brand-new taxonomy entries sit at the sharpest end. Persistent memory poisoning lands at expert No. 4 and incident No. 16, MCP tool interface exploitation at expert No. 7 and incident No. 16, each with an incident interval of 6 to 20 that spans most of the taxonomy.Public 2026 CVEs exist for both. On MCP tool interfaces, the Azure Data Explorer MCP Server carried KQL injection, and the CVE record describes it allowing “an attacker (or a prompt-injected AI agent) to execute arbitrary KQL queries against the Azure Data Explorer cluster,” scored 8.3 High. Kong’s Konnect MCP Server shipped an indirect prompt injection that lets a remote attacker steer the server into executing unintended API requests, the exact failure the MCP entry names.Agent memory has its own record. An agent harness, Ruflo, exposed unauthenticated MCP bridge endpoints that let a network attacker obtain a shell, read provider API keys, and poison the learning store, rated 10.0 Critical.The record is so thin and uncertain that the model cannot place either entry within 14 rank positions. A team waiting for advisory volume to justify a control on agent memory or an MCP tool boundary would still be waiting while the CVEs accumulate at Critical and High.Lambros makes the budget case in operational terms. Poisoned memory “doesn’t announce itself,” he said. It looks like a procurement agent told once that invoices from a given supplier under $50,000 clear without a second signature, and because the agent remembers, every approval after that looks like the process working. “Nobody files an advisory for that, because nobody knows it happened. A count of zero is measuring your blindness, not your safety.” The argument he says a CFO will sign off on is timing, since memory and tool permissions get wired into these systems once, early, and everything else sits on top of them. “Build it in now and it’s a rounding error. Come back in two years and you’re re-architecting and re-training your systems.”The authors flag their own measurement problems first The expert side is thin. “The expert signal is a practitioner survey: about 29 respondents scored each candidate risk on importance,” the authors write. Twenty-nine votes set the ranking that carries three-quarters of the published list’s weight, the compression point for OWASP’s more than 25,000 community members.On the data side, the classifier is the weak joint. Precision “varies sharply across entries, from 93% (LLM01, LLM03) down to 13% (LLM08),” four entries fall below 50%, and the base classifier “never predicts ‘out of scope’ and files every incident into some category, including the roughly 38% of the gold set that belongs in none.”The authors name the central limitation themselves. One reviewer adjudicated all 1,200 gold-set incidents and overrode the model consensus on 553 of them. “A single annotator cannot measure inter-rater reliability,” they write. “The single-author gold set remains the central limitation.”Lambros lays the weak kappa at the feet of the taxonomy itself. “That number is telling you about our categories, not about our experts,” he said. When the people who wrote a taxonomy cannot reliably sort incidents into it, he argues, “a weak score on the ordering of those buckets is a fact about the buckets.”A better classifier will not fix the disagreement. A pre-registered bake-off of four frontier models produced no winner. None beat the incidence floor’s balanced accuracy of 0.863, and a ground-truth check left the floor’s ordering in place at a Spearman correlation of 0.918. The authors published the engine and artifacts on GitHub for anyone to rerun.The robustness result tested only one side of the gap. Every check behind the abstract’s word “robust” runs on the incident side, showing the incident-derived ranking stays put when the labeling machinery changes, and none of it touches the 29-vote survey. A board that hears “robust” will assume validated, yet the record supports only stable.What the published list did with this OWASP shipped the GenAI LLM Top 10 2026 on August 4, the first edition to fold incident data into the ranking, weighting the practitioner vote at 75% and the incident corpus at 25%. Prompt injection stayed at No. 1, misinformation moved up two places, excessive agency climbed from No. 6 to No. 3 as the entry where the two signals agree most clearly, unbounded consumption rose four spots to No. 6, and improper output handling fell from No. 5 to No. 10, the largest drop.Wilson declines to defend the blend as arithmetic. “There is nothing magical about a 75/25 weighting,” he said, “or about reversing it to 25/75. The value of the data wasn’t that it gave us a mathematical answer; it changed the conversation.” The excessive agency entry is where that conversation landed hardest for him. “If I were a CISO evaluating a new agentic deployment today, Excessive Agency is where I would start,” Wilson said.Lambros would go further next cycle, a view he flags as his own and separate from the working group. The blend hands the same 25% incident weight to every category, while the hand-checked classifier precision runs from roughly nine in 10 on prompt injection and supply chain down to roughly one in eight on vector and embedding weaknesses. A quarter of the weight on the first rides on something solid, he argues, and the same quarter on the second rides on noise. “The ratio should track how well we actually measure each category,” Lambros said.Why this lands now Ivanti’s 2026 State of Cybersecurity research found 87% of security teams call adopting agentic AI a priority and 77% report at least some comfort letting AI act without human review. Teams are signing off on agent autonomy while the expert ranking of what can go wrong with those agents shows no statistically detectable agreement with the incident record.What to do with this on Monday The behavioral change is narrow and it is the whole point.Use the OWASP LLM Top 10 as a coverage map, not a queue. The rank positions carry 29 votes and a corpus whose own authors call the agreement weak, so build your own priority order from your own exposure: production reach, breach-notification data, and controls that have actually been tested. Lambros draws the funding line the same way. “I’d prioritize spend where the expert vote and the incident record point the same direction, because that’s two independent witnesses agreeing,” he said. “Where they split, stop letting the ranking allocate your money and go look at what your own systems are doing.”Log what your AI systems are actually doing, field by field. The prompt that went in, what came back out, the documents pulled to build the answer, the tools called and the arguments passed to them, and the model’s confidence score on every response. Confidence is the field Lambros would fight for, because most security leaders do not realize it is measurable, and it is where the attack surfaces. “A model running on a poisoned instruction doesn’t act broken. It acts certain,” he said. “Certainty is what your monitoring treats as a healthy system.” The cost is a sprint or two of engineering. The constraint is a person, because a SIEM does events and these are trends. “Somebody has to analyze those trends every week and say whether a drift means anything, and most security teams have nobody who can.”Stop expecting scanner output to reproduce the Top 10’s order. Scanner findings live on the incident side of the gap, counting what got disclosed rather than what a deployed system should fear, and the classifier bake-off shows a smarter model does not close that distance. The test that sees prompt injection is an adversarial one run against the live system, paired with Wilson’s authorization gate so the change an injected agent proposes is never the change it can execute.Fund the thin-record categories on architecture, not incident volume. Agent memory and MCP tool boundaries sit at expert No. 4 and No. 7 with incident intervals spanning most of the taxonomy, and the CVEs that do exist are landing at High and Critical. Kayne McGladrey, an IEEE senior member who advises enterprises on risk, put the funding logic bluntly in an interview with VentureBeat. “Anything that seems to have a cybersecurity flavor is generally put into the cybersecurity risk category, which is a complete fiction,” McGladrey said. “They should be focused on business risks, because if it doesn’t affect the business, like a financial loss, then nobody’s going to pay attention to it, and they will not budget it appropriately.” A rank number from a 29-person vote is a weaker budget argument than the business system the agent touches.Steal McGladrey’s baseline test for the AI systems themselves. “If you wouldn’t expose your database to the public internet without identity and access controls, why would you do that for your AI model?” he said in CSO Online’s analysis of 2026 breach costs.The board question for the next meeting is short. If our AI risk ranking came from a 29-person vote and a corpus that disagrees with it, what are we actually using to decide which controls get funded next year?

Microsoft Teams is adding a policy that lets IT admins automatically block detected external meeting bots instead of relying on organizer approval. The post Microsoft Teams’ New Policy Lets Admins Automatically Block Meeting Bots appeared first on TechRepublic.

A power plant was shut down for four days in July due to an Iranian-linked cyberattack

Apollo Global Management confirmed a social-engineering breach exposing sensitive personal data amid a wider hacking campaign targeting financial firms. The post Apollo Confirms Data Breach Amid Cyberattacks Targeting Financial Firms appeared first on TechRepublic.

The Medusa Ransomware gang has breached more than 500 organizations since June 2021.

Apollo Global Management experienced a data breach due to a social engineering attack, which affected sensitive, personally identifiable information.

Microsoft has yet to set a firm Exchange Server SE CU1 release date as engineers work through AI-assisted security findings and ongoing patch releases. The post Microsoft Exchange Server SE CU1 Delayed Amid AI-Assisted Security Reviews appeared first on TechRepublic.

ChatGPT for Teens adds stronger protections for users ages 13 to 17, but parents still face limits around monitoring, age prediction, and AI safety. The post ChatGPT for Teens Adds New Safeguards — but Safety Gaps Remain appeared first on TechRepublic.

Google’s new Android verification flow adds a 24-hour wait for apps from unverified developers as broader identity checks approach. The post Google Tightens Android Sideloading: Unverified Apps Now Face a 24-Hour Wait appeared first on TechRepublic.

ICE warned employees against using Meta smart glasses on duty as DHS seeks $7.5 million to develop biometric-enabled prototypes for field agents. The post ICE Warns Employees Against Meta Smart Glasses appeared first on TechRepublic.

Windows 11 24H2 Home and Pro support ends Oct. 13, 2026. Here’s what users and IT teams should know about upgrading to Windows 11 25H2. The post Windows 11 24H2 Home and Pro Support Ends Oct. 13: What Users Should Do appeared first on TechRepublic.

Security magazine takes a look at six data breaches from July 2026.

Amazon’s less-detailed order emails protect purchase data but may make phishing harder to spot. Learn how to verify order messages safely online. The post Amazon’s Order Email Privacy Change Creates a Potential Phishing Trade-Off appeared first on TechRepublic.

On August 19, OpenAI confirmed a ChatGPT outage affecting users worldwide.

Windows 11 hotpatching can install security updates without reboots. Learn who qualifies, how the update cycle works, and what IT teams should expect. The post Windows 11 Hotpatching Explained: How Much Does It Really Reduce Reboots? appeared first on TechRepublic.

450.2 gigabytes of data were exposed in a public database with no password protection or encryption.

The Department of Justice (DOJ) decided last month that TikTok was no longer to be banned from government devices.

A hacker claimed to have stolen 3.6 million Azure account records from major organizations. Security leaders discuss.