Close the gaps agents open in your attack surface.

Hidden instructions that override your agent's rules, permissions that let it do more than it should, tools it can be tricked into misusing, secrets that leak out — these are the risks traditional application security doesn't cover. We find them before an attacker does, then close them with permission design, protective checks, and adversarial testing built for systems that can act on their own.

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Mapped to a standard, not a hunch.

Findings map to OWASP LLM Top 10 and OWASP AISVS, backed by the same secrets and observability tooling your security team already trusts.

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Recognize the symptoms

The symptoms that show up first.

Your firewall and API auth were built for software that doesn't act on its own. None of them catch these.

  • A document an agent read contained hidden instructions it followed as if you'd written them
  • An email agent can also delete records because nobody scoped its permissions
  • A jailbreak extracted an API key straight out of a model completion
  • Nobody can say which system is calling which model, or with what credentials

Seven risks traditional security doesn't cover.

  • The moment your agent can trigger workflows, call APIs, or modify data, it needs security controls traditional app security doesn't provide.
  • Once an agent can act on its own, it opens up new ways to attack it: what it outputs is just text, not code, so passwords, access keys, and personal data can end up buried in what it's given, what it says back, or what gets logged.
  • You can lock down the database, but if an agent has access to it, a malicious instruction can trick it into pulling out data or deleting records — instructions it never should have trusted in the first place.
  • When something unexpected happens, you need an audit trail: which prompt triggered it, which tool was called, did it follow policy.

The seven risks

Prompt injection — someone directly tries to override its rules, or hides malicious instructions inside content it reads
Too much access — an agent that can do more than its actual job requires
Tool abuse — an attacker gets it to call a tool with the wrong arguments, or chains actions in ways nobody planned for
Data leakage — passwords, access keys, or personal data ending up exposed in what it reads, writes, or logs
Unaudited access — credentials shared across services with no record of what's calling what
Missing identity boundaries — an agent that can act across different customers', users', or sessions' data when it shouldn't
Unchecked actions — something significant happens with no approval step and no way to undo it

Security designed in, not just tested for.

Five steps, not a one-time scan.

Threat model

Map your system against the industry-standard list of AI security risks (OWASP LLM Top 10 and AISVS) to know which threats actually apply to you.

Test for injection

We test for prompt injection — both direct and hidden inside content it reads — plus attempts to break its rules, built into a repeatable test suite.

Design permissions

We define exactly which agent can call which tool, separate tools that only read data from tools that change it, and require approval for sensitive actions.

Add guardrails

We add checks at every stage — what goes in, what gets retrieved, what gets executed, what comes out — to block injection attacks, tool misuse, and leaked secrets.

Red team and document

We try to break it with attacks nobody's tried yet, then hand you a report you can also use as compliance evidence.

From our experts.

Writing on safe agent rollout and production failure modes.

Production & Engineering

How to Roll Out an AI Agent Safely

Production agents never deploy all at once. The staged rollout gate — shadow, canary, expansion, stable — that Anthropic, Google Cloud, and production teams actually validate against.

13 min read

Production & Engineering

AI Agent Failure Modes in Production: The Four That Actually Kill Systems

The four failure modes that repeat across production deployments: tool-calling errors with cascading impact, infinite retry loops burning budgets, hallucination cascades in multi-agent systems, and silent quality drift.

14 min read

Production & Engineering

Why Most AI Agent Pilots Never Reach Production

79% of enterprises claim AI agent adoption. Only 11-17% actually run them in production. Here's why the gap exists, and the infrastructure patterns that close it.

13 min read

The same nine-phase lifecycle, applied to security.

Discovery through knowledge transfer, with a threat model and a test suite instead of a checkbox.

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Common questions.

Know what your agent is vulnerable to before an attacker does.

A security review gives you a threat model, test results, and compliance evidence. You'll know what breaks, how to fix it, and what to tell customers about your controls.

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