AI Red Team Audit

AI red team, chained attack paths.

Adversarial testing across prompt injection, jailbreaks, tool misuse, and chained attack paths. Structured, reproducible, written up with attack chains you can hand to engineering.

LLM apps · agents · long-running loops

Request an intro call
Why this exists

Single prompts are easy. Chains aren’t.

Automated benchmarks test one prompt at a time. Real attackers compose: indirect injection from a fetched document, followed by a system-prompt leak, followed by tool-call escalation, followed by data exfiltration through an approved channel. That’s a chain. Our red team finds chains.

Scope

What we test.

Direct prompts

Prompt injection and jailbreaks

Adversarial prompts that bypass system instructions, force off-policy output, leak the system prompt, or escalate to privileged tool calls.

Indirect

Indirect prompt injection

Injection via retrieval sources, tool outputs, file contents, and any data the model reads as untrusted input but treats as instruction-bearing.

Agent loops

Tool misuse and chained attacks

Forcing the agent to call tools in dangerous combinations, exfiltrate data via approved channels, or persist state across sessions in ways the operator didn't intend.

How pricing works

Scoped to your system.

Red team engagements are sized to attack surface (number of tools, agent depth, integration surface) and timeline. Talk to us for a scope and quote.

Request an intro call
FAQ

Questions.

Structured adversarial testing against your LLM application or agent. We map the attack surface, build attack chains across prompt injection, model abuse, and tool misuse, then deliver a written report with reproductions and remediations.

The application. The model behavior is what it is; the audit looks at how your prompts, tool definitions, retrieval sources, and guardrails compose into something exploitable.

First-class scope. Long-running agents have unique risk classes: state persistence attacks, multi-step plan manipulation, and tool-output injection that compounds across turns.

Yes — and we have a dedicated MCP Security Audit page if your engagement centers on MCP server design. The red team engagement covers MCP only as one component of a broader attack surface.

Written report with attack chains, reproduction prompts and tool sequences, severity rationale, and remediation guidance. Plus fix verification after you patch.

Two to four weeks depending on attack surface and agent complexity. Single-shot tests faster; ongoing red-team retainers are available.

From our research

Go deeper.

Discipline

AI red teaming is no longer optional

The threat model shift that makes red teaming the default for any production LLM deployment.

Method

AI red teaming with OpenClaw

Our internal red-teaming methodology, the agent harnesses we build, and how we reproduce attacks deterministically.

DeFi

AI and DeFi: prompt injection on a trading agent

Live example of how a prompt injection against an autonomous DeFi agent compounds into on-chain loss.

Other services

Need something else?

Parent

AI security audits

Back to the parent service — see the full scope, methodology, and ways to engage.

Sibling

MCP security audit

For MCP server design and tool boundary testing specifically.

Subscription

Security Audit Subscription

Ongoing AI security review as your LLM app or agent evolves week to week.

Ready when you are

Ready to red team?

Send us your LLM app or agent system and a target date. We’ll come back with a scope and a quote within 24 hours.

Request an intro call