Secure AI Agents: US-UK Playbook for SaaS Teams

A 50% exploit-chain success rate is the kind of number that wakes up a security team fast. In mid-2025 testing of an agentic AI product, an external evaluator demonstrated that chaining “classic” software bugs with AI agent hijacking techniques could bypass protections often assumed to be strong enough on their own. The good news: the issues were reported and fixed within one business day. The bigger news: this is what healthy AI security infrastructure looks like when it’s practiced, not just promised.

This post sits in our “AI in Defense & National Security” series because the same systems powering customer support bots and internal automation are now relevant to cyber defense, critical infrastructure, and biosecurity. If you run a SaaS platform or digital service in the U.S., the most practical takeaway is simple: secure AI deployment is becoming a shared national capability, and the organizations that treat it that way will move faster with fewer surprises.

What follows is a plainspoken playbook built from recent US-UK public-private collaboration patterns: how external red teams found real weaknesses in AI agents, how biosecurity testing works when it’s continuous, and what startups and SaaS operators should copy—starting this quarter.

Public-private AI security is becoming “table stakes” infrastructure

AI security partnerships between companies and government standards bodies are now part of the U.S. digital ecosystem, not an optional PR exercise. The reason is straightforward: frontier models and AI agents can affect national security-relevant domains (cyber operations, chemical/biological knowledge, identity and access, and more). When these systems sit inside widely used digital products, their security posture becomes everyone’s problem.

Two institutions illustrate the direction:

• US CAISI (Center for AI Standards and Innovation): focused on rigorous evaluations and practical testing in security-relevant domains.
• UK AISI (AI Security Institute): known for deep technical safety testing, including targeted red teaming against misuse (like biological misuse).

The important shift isn’t who is involved—it’s how the work is done:

• Early access for evaluators (enough time to learn architecture)
• End-to-end product testing (not just model Q&A)
• Fast feedback loops (engineers actually patch things quickly)
• Repeatable methods that can become standards

If you’re building AI-powered digital services in the United States, this is the model to watch because it’s a preview of where procurement, enterprise due diligence, and regulation are heading.

Myth-bust: “Model safety” alone doesn’t secure AI agents

A lot of teams over-index on model alignment and moderation. Necessary, yes. Sufficient, no.

Agentic AI changes the risk equation because it can:

• Take actions (click, type, send, buy, deploy)
• Use tools (browsers, terminals, APIs)
• Access user sessions and third-party services
• Persist context across steps

That means your threat model starts looking less like “chatbot abuse” and more like endpoint security + web app security + identity security, with a new AI-specific twist.

What the CAISI-style red team teaches about agent security

The strongest lesson from the agent red-teaming collaboration is that attackers don’t need a single perfect exploit—they need a chain. External evaluators identified two novel vulnerabilities that, under certain circumstances, could allow a sophisticated attacker to bypass protections and impersonate a user on websites they were logged into during an agent session.

At first glance, even experts believed the issues were unexploitable because of product security measures. Then the evaluators combined traditional vulnerabilities with an AI agent hijacking approach to bypass those measures and demonstrated an exploit chain with ~50% success.

That 50% number matters for operators. Anything that works half the time becomes automatable, repeatable, and scalable.

Why “classic” appsec still matters more than you think

If you’re a SaaS team, this should feel familiar: vulnerabilities rarely arrive as a single dramatic failure. More often, it’s a sequence:

1. A minor input-handling weakness
2. A session boundary you assumed was hard
3. A permissions check that’s correct in one layer but missing in another
4. A workflow edge case that creates unintended authority

AI agents add new links in the chain:

• Prompt and instruction injection inside tool outputs
• Confused-deputy failures (agent obeys malicious content while holding user privileges)
• Tool routing manipulation (steering to a more permissive pathway)
• UI spoofing inside agent-driven browsing flows

Opinionated take: if your agent can operate a browser, you should treat every page it reads as potentially hostile content.

The “one business day” patch is the standard to aim for

Fast fixes aren’t luck—they’re organizational design.

If a sophisticated evaluator can go from idea → proof-of-concept → exploit chain, you need the ability to go from report → reproduce → mitigate → deploy quickly. For SaaS leaders, that means having these pieces ready before you “go agentic”:

• A defined security triage lane specifically for AI/agent issues
• Repro environments that mirror production tool permissions
• Feature flags or kill switches for tool access and risky workflows
• Logging and forensics that capture tool calls and decision traces (without collecting unnecessary sensitive data)

Biosecurity red teaming: what “ongoing” testing looks like in practice

Continuous red teaming beats one-off launch gates. In biosecurity testing, evaluators didn’t just check a model at release time; they ran a months-long collaboration aimed at finding universal jailbreaks against bio-misuse safeguards across the full product experience.

There are two angles SaaS and digital service providers should notice.

1) End-to-end product testing finds what model tests miss

Model-only evaluations miss the practical paths real users exploit:

• Content being inserted into uploads, docs, or transcripts
• “Exfiltration” routes through exports, summaries, or tool outputs
• Configuration gaps where moderation isn’t enforced consistently

In the collaboration described, end-to-end testing uncovered configuration vulnerabilities where malicious content could be input or extracted without triggering moderation. That’s not an abstract “alignment” issue. It’s an integration issue—exactly the kind that shows up in fast-moving SaaS stacks.

2) Rapid feedback loops are the real moat

Weekly cadence matters because security fixes change attacker strategy. The loop looks like this:

• Evaluator probes system
• Team patches safeguards (engineering + policy + classifier training)
• Evaluator retests and adapts

Over time, successful universal attacks require higher sophistication and generate more monitoring signals—making detection and enforcement more effective.

The stance I’d take: if your AI safety effort doesn’t change week to week, it’s not a program; it’s a document.

Why this matters for U.S. digital services and SaaS providers

Secure AI systems are now a prerequisite for scaling AI-powered services in the United States. Not because everyone wants more paperwork, but because AI agents and advanced assistants touch:

• Customer data (privacy and compliance)
• Financial actions (fraud and account takeover)
• Enterprise workflows (supply chain, IT, HR)
• Cybersecurity operations (defense tools can be turned into attack tools)

In national security terms, this is “dual use” reality: the same capability that speeds up legitimate work can also accelerate misuse.

For SaaS providers, the commercial translation is blunt:

• Enterprise buyers increasingly ask about AI security posture, not just features.
• Regulators and auditors will expect controls that look like governance frameworks, not ad hoc prompts.
• If you can’t explain your agent’s permission boundaries, you’ll lose deals.

A practical governance framework you can adopt without slowing down

You don’t need a defense agency to start acting like a serious operator. Here’s a lightweight framework I’ve found effective for AI agent security in SaaS:

1. Tooling boundary map (one page): list every tool/action the agent can take and the exact permission model.
2. Least-privilege by default: start with read-only tools; require explicit user confirmation for high-risk actions.
3. Session hardening: short-lived tokens, scoped credentials, and clear separation between browsing context and authenticated sessions.
4. Defense-in-depth moderation: don’t rely on a single classifier; place checks at input, tool output, and pre-action.
5. Red team as a cadence: monthly internal red teaming + quarterly external evaluation for agent workflows.
6. Monitoring you can enforce: define what triggers human review, rate limits, or bans; measure time-to-detect and time-to-response.

If you want one metric to operationalize: mean time to mitigate (MTTM) for AI/agent vulnerabilities.

“People also ask” (and how I answer it)

Are AI agents a bigger cybersecurity risk than chatbots?

Yes. AI agents expand the blast radius because they can take actions using tools and user privileges. The security problem shifts from “harmful text” to “harmful operations.”

What is AI agent hijacking in plain English?

It’s when an attacker steers an agent’s behavior—often through malicious content the agent reads—so the agent performs actions the user didn’t intend, sometimes while holding the user’s authenticated access.

Do startups need government-style evaluations?

You may not need government evaluators, but you do need the discipline: independent testing, documented findings, fast patch cycles, and measurable controls. The smaller your team, the more you benefit from a structured loop.

What to do next: an AI agent security checklist for Q1 planning

If you’re heading into 2026 with AI automation goals, bake security in now—before the agent touches real accounts.

• Run an “agent permission review”: every tool call, every token, every high-risk action.
• Add confirmation gates for money movement, account changes, external communications, and data exports.
• Treat tool outputs as untrusted: sanitize, constrain, and validate what the agent is allowed to do next.
• Instrument audit logs: capture user intent signals, tool calls, and policy decisions.
• Schedule an external red team: focus on exploit chains, not isolated prompt tricks.

Public-private collaboration has shown something encouraging: when evaluators get meaningful access and teams respond fast, security improves in real products used by real people. That’s the direction the U.S. digital economy is moving—especially in defense-adjacent and national security-sensitive sectors.

The forward-looking question I’d keep on your roadmap: When your agent can act like a user, are you protecting it like a user—or like a feature?