AI & agent security

Test your AI agents before attackers do

Prompt injection is still unsolved. Once an agent can act inside internal systems, weak guardrails become a direct path to compromise.

We help teams assess, design, test and harden AI agent systems.

Talk to us

Trusted with critical software by NASA, Boeing, the Ethereum Foundation, Solana and Stellar since 2010.

Untrusted input
  • Emails
  • Tickets
  • Docs
  • Web
  • Code
  • Slack
Your agentDoing exactly what it was asked
Real actions
  • Tools
  • APIs
  • Databases
  • GitHub
  • Cloud
  • CRM
  • Production

The risk goes beyond just bad answers

Agents get hijacked

Hostile instructions hide in documents, emails, tickets, web pages, PDFs and Slack.

By default, an agent obeys them.

Agents exceed their authority

Give an agent access to tools, credentials and production systems, and prompt injection stops being a content problem.

It becomes an authorization failure.

Agents cause real damage

A compromised agent can leak data, delete files, expose secrets, send messages and run unsafe code.

Most guardrails were not built for adversarial tool use

The system prompt tells the agent not to do that.
Untrusted context can talk straight past the system prompt.
The model is good at following instructions.
Attackers confuse the instruction hierarchy and turn that into an advantage.
The agent only uses approved tools.
Approved tools can still be called in unsafe sequences, with unsafe arguments.
There is a human in the loop.
Approval steps can be vague, bypassed, or flooded with misleading context.
We log everything.
Logs help after the fact. They do not stop the agent taking the wrong action.

We help make agent behaviour defensible

AI agent security assessment

We review the design, map the attack surface, run light adversarial testing, and find where the agent has unsafe permissions, weak boundaries or behaviour your engineers never intended.


You get a concise report with findings rated by severity, and a prioritized plan for what to fix next.

Best for

Teams already building, piloting or deploying agents

Agent guardrail design

We design stronger control layers around what the agent is allowed to do:

  • Agent containment and scoped permissions
  • Policy-based authorization with systems like Cedar
  • Data honeypots and prompt validation
  • Human approval gates and defensive monitor models

The result is an agent that is harder to hijack, harder to misuse and easier to trust.

Best for

Teams moving from prototype to production

Adversarial testing and red teaming

Our engineers bring a barrage of adversarial techniques, fuzzing strategies, hostile inputs, malicious context and tool-abuse cases to find out whether your guardrails actually hold.


The result is evidence of where the system breaks, while you still have time to change it.

Best for

Teams that need confidence before a launch, an enterprise review or a wider rollout

Ongoing AI security support

Agent security moves too quickly to treat as a one-time checklist. We stay available as models, tools and attack techniques change:

  • Periodic retesting
  • Briefings on new attack classes
  • Review of major architecture changes
  • Implementation support
  • Fast answers when a new attack technique lands
Best for

Teams that want expert backup while they keep shipping

Tailored for technical teams moving fast with AI

Our work is designed for companies building, deploying or adopting AI agents before they have an internal AI security team.

A good fit

  • AI agent startups
  • SaaS companies adding agentic features
  • DevOps and developer tool companies
  • Fintech, blockchain and payment infrastructure
  • Healthcare, legal and finance automation
  • Engineering orgs running Cursor, Claude Code, Copilot or other coding agents
  • Teams preparing for an enterprise security review

Especially if your agents can

  • read customer or internal data
  • call tools or APIs
  • edit files or code
  • run shell commands
  • reach GitHub, CI/CD, cloud, CRM, support tools or databases
  • send external messages
  • modify or delete records
  • act on behalf of your users

Why Runtime Verification

“The question is not whether the model sounds safe. It is whether the system around it enforces what must never happen.”

Everett HildenbrandtCEO of Runtime Verification

That is a formal methods question. We specify the properties a system must hold and prove the code holds them, and we bring the same discipline to agents.

Do not wait for an agent incident to find the boundary

Let us find the weak points first.

Talk to us about your agents