Insecure Agents

Gabriel, Staff Security Engineer at Yalo, joins us live at AI Security Summit in San Francisco to explain how you secure a software factory whose product is other agents. Yalo builds AI sales agents for its customers, and its newest tool is an agent that builds agents, so securing that one agent means securing the factory. Gabriel walks through the layers: role-based access and authorization on APIs and services that predate agents, so anyone prompting the factory gets only the scope they should have; agent firewalls at the edge that redact PII and block off-limits conversations in microseconds, backed by asynchronous LLM judges; red teaming against prompt injection and the OWASP LLM Top 10; and scanning every skill for leaked credentials and exfiltration paths. He's also candid about what feels like a hard problem to solve: blocking a misbehaving agent at runtime.

Josh Woodruff, Co-Chair of the Zero Trust Working Group at the Cloud Security Alliance and Founder and CEO of Massive Scale AI, joins us to talk about the Agentic Trust Framework, the open source zero trust model for AI agents he donated to CSA. His framing is simple on purpose: treat every agent like a new hire. It goes on the roster, gets a badge, starts as an intern, and earns more freedom through demonstrated reliability, with instant demotion when it misbehaves. Zero trust taught us to check who you are and what you can reach on every request. Josh argues agents need one more check on top of that: what are you doing right now, and is that allowed at this moment? We get into why agents running under a human's identity break your behavioral baselines and turn every kill switch into a work stoppage, why intent is a useful signal but the checkpoint still has to be able to say no, and how ATF fits alongside AARM and Agent Baseline as the field converges.

Andy McMahon, Director and Principal AI & MLOps Engineer at Barclays, joins us to talk about what it actually takes to move an agent from prototype to production inside a 200-year-old bank.His name for the core problem is the fishbowl: every platform, vendor, and runtime arrives with its own identity provider and its own auth, and you are left trying to make the fishbowls speak to each other. The fix was landing on one persistent concept of agent identity that ties the initiating client, the agent, and any delegated permissions together.We get into the three foundations he builds on (gateways, registries, and runtimes), why kill switches have to be layered from credential revocation through gateway denial to killing the runtime entity, why deciding when to pull one is still hard, and why an incident where agents doctored their own logs means the audit log must sit outside agent control.

Recorded live at Browserbase Navigate on September 10, this panel discusses the shift of agents as pets to agents as cattle: from hand-managed personal agents with copied API keys to fleets of agents that serve an entire enterprise and act on the open web. Moderator Allie Howe is joined by Ian Livingstone, CEO of Keycard, Peyton Casper, Product Lead, Identity at Browserbase, and Ivy Lee, VP Product, CMS Agentic Commerce at Visa. We get into why you can't revoke an agent's access if you can't identify it (a requirement the Agent Baseline reference architecture for production agents calls out), how Web Bot Auth gives browser agents a cryptographic identity that ties traffic back to a person or organization, how Visa's Trusted Agent Protocol and payment tokens let merchants tell a payment agent from a bad bot, and why the answer to "good intent, wrong action" is permissions scoped to the task, not to the human who launched the agent.

Max Pollard, CEO and co-founder of Cotool, joins us to talk about what it actually takes to let blue teams respond at machine speed. Cotool builds agents for detection and response, and Max walks through the crawl, walk, run progression his customers follow: start read only, route every consequential action to a human through an approval hook in Slack or Teams, count how often the agent was right, and only then loosen the leash. His honest read is that very few teams reach the run phase, because humans can't trust agents enough to take themselves out of the loop. We get into why the real blocker is less about trusting the model and more about data annotation, and whether the agent knows at runtime that the server it is about to contain powers a million dollars a day in transactions. Max also covers how Cotool scopes agents at the API endpoint level at config time, why the audit log has to record that an agent acted and on whose behalf even when no human prompted it, and how his team evaluates cost efficiency, instruction adherence, and behavior across 20, 30, or 40 agents running in production.

Every security leader knows that developers will work around security tooling when they can if it gets in the way of their job. The truth is, employees don't get paid to be secure; they get paid to do their job.Joel de la Garza, partner on the infrastructure team at a16z and former CISO of Box, tells us that the moment security gets in the way of what people want to do, security loses.For a lot of teams, this has meant tolerating uncomfortable security gaps to get the value agents can bring. At Keycard, we call the underlying bind the trilemma of autonomy, capability, and security: teams can only pick two.Joel walks through what securing agents looks like in practice, from a consumer agent logging into an auction site through a browser with every alarm bell ringing to a16z's own coding agents opening tunnels out to Cloudflare to route around egress controls in a secured GCP cluster.We get into why least privilege and agent performance pull against each other, and why some of security's first principles have to be rethought rather than reasserted. We also talk about the pressure CISOs face as they're expected to say yes to agents and lead the agent rollout.Joel also shares his love for Grok Bot and the need for model providers to give blue teams the same advantages attackers now have.

Most people that architect an AI agent think to ask a binary question: is this tool call allowed, yes or no? Michael Davis, Global Chief Security Architect at J.P. Morgan, argues it's never that simple. He walks through the four dimensions robotics uses to decide whether it is safe for an arm to move (the state of the system, the state of the environment, the quality of the decision-making, and the uncertainty of the action itself) and maps each one to agents: did the agent gain tools or network connections it did not have before, is it still moving semantically toward its goal after 30 tool calls, and what has the agent already tried. We get into why  if you think you need memory, you're probably not thinking of your problem the right way. We also explore software factories and why they need to operate as a  cumulative progressive process versus a one shot of go build me this SaaS. Over the course of the episode we piece together what a good reference architecture for agents could look like.

Jet Anderson, is a Distinguished Engineer at GEICO and leads the transformation of their product security function. He joins us to discuss the agentic SDLC and why most of what security teams do has to change while the first principles should stay the same. We get into why static analysis and human triage no longer keep pace when models write the code, why we need new AI infrastructure, and why the Hugging Face sandbox escape was a decade-old Kubernetes misconfiguration found at machine speed. Jet tell us the story of when he asked his own agent to "wrap this up" and it merged the PR and did an unauthorized production deploy. He walks through the pre-commit hooks and branch protections he built to prevent this from happening again so that deploys still get an approval gate and an audit trail. He also explains why teams that skip ideation, design, and specification to go straight to code end up with less secure software the more they iterate, and why the unglamorous answer is doubling down on least privilege, egress control, and build containment.

Travis McPeak, security lead at Cursor, joined us at Black Hat to talk about what causes coding agents to become problematic and what he's done to keep them on track. What he sees most is the main agent trying hard to solve your problem, getting stuck, and then getting creative. He calls it thrash, and most of the time when somebody says an agent deleted their database, that is what happened. This episode gets into why static controls like block lists and WAFs break down against an agent whose whole job is to find a way through, why an agent can't monitor itself, and how Cursor's auto review agent works: every tool call the main agent is about to make goes to a separate reviewer that does not care about the main goal at all and only enforces its rules. Travis also explains why the team had to carefully word the feedback so the main agent would not spiral, why blocking an action can cause the exact thrash you were trying to prevent, and why least privilege finally scales now thanks to new solutions.

Alissa Abdullah, PhD ("Dr. Jay"), Deputy Chief Security Officer at Mastercard, and Arjun Ramakrishnan, Senior Principal Cybersecurity Architect at Mastercard, join us live from Black Hat to explain why access models built for humans start to break when agents act on their behalf.You can't provision access for an agent the same way you provision it for an employee, and just-in-time authorization takes on a new meaning when the consumer operates at machine speed. Dr. Jay and Arjun explain why every tool call and action needs to be authorized, why that's difficult to enforce, and what security teams need when an agent goes rogue, including the ability to revoke access instantly through a button or API call.We also get into Mastercard's three-layer framing of security for AI, from AI, and with AI, and why agent governance needs to move from periodic review to real-time enforcement.