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03 Reinforced Red

Somebody will try to break your agent. Make it us.

Attacker agents probe your system every night. Human specialists run deep engagements before launches. Every finding ships with a reproducible transcript, a fix and a regression test.

findings · claims-agent · week 314 open
CriticalInjected PDF triggers payout to new IBANRL-0412
HighClaim history leaked via markdown image URLRL-0419
MediumCrescendo attack yields medical diagnosisRL-0423
MediumPolicy number enumeration via error textRL-0427
FixedSystem prompt disclosed in base64RL-0398
All fixed findings re-tested nightly2,904 attack runs today
01 / Two modes

Machine breadth. Human depth.

Continuous

Attacker agents, every night

Our attacker models generate and adapt thousands of attacks against your staging environment, learning from what partially worked. New techniques from our research team are added weekly.

  • Indirect injection through every content source
  • Multi-turn jailbreaks and crescendo attacks
  • Tool misuse, privilege escalation, data exfiltration
  • Regression tests for every past finding
Engagements

Specialists, before it matters

Two- to six-week engagements led by offensive security researchers and domain experts. Suited to launches, new tool permissions, regulatory reviews and frontier model releases.

  • Threat modelling workshop with your team
  • Manual exploitation of business logic
  • Domain misuse testing (bio, cyber, finance, health)
  • Executive readout and fix verification
02 / Method

How an engagement runs.

  1. Scope and threat model, days 1–3

    Assets, actors, permissions and the outcomes you can't afford. We agree severity definitions in your language, not ours.

  2. Reconnaissance and automated sweep, days 3–7

    Attacker agents map the system's behaviour and run our full attack library to surface the obvious weaknesses quickly.

  3. Manual exploitation, weeks 2–3

    Specialists chain weaknesses into realistic attacks, the kind that end up in incident reports.

  4. Report, fix, retest

    Reproducible transcripts, severity, root cause and recommended controls. We retest after you fix and hand over a regression suite.

03 / Attack library

What we test for.

FamilyExamplesMaps to
Prompt injectionHidden instructions in email, PDF, HTML, images, tool output; delayed triggersOWASP LLM01
Sensitive data disclosureSystem prompt leakage, cross-tenant retrieval, markdown/URL exfiltrationOWASP LLM02, LLM07
Excessive agencyUnauthorised payments, emails, deletions; confused-deputy tool chainsOWASP LLM06
JailbreaksRole-play, many-shot, crescendo, obfuscation, low-resource languagesMITRE ATLAS
Misuse & dangerous capabilityBio, chem, cyber uplift; fraud and impersonation assistanceNIST AI 600-1
Integrity & misinformationFabricated citations, unsupported medical/legal/financial claimsOWASP LLM09
04 / Questions

Red teaming FAQ.

What is AI red teaming?

AI red teaming is structured adversarial testing of a model or AI system to find ways it can be made to behave unsafely: leaking data, taking unauthorised actions, producing harmful content or bypassing policy. Good red teaming starts from a threat model and ends with fixes that are verified by retesting. Read our AI red teaming methodology.

How is automated red teaming different from a human engagement?

Automated red teaming uses attacker agents to run thousands of adaptive attacks continuously and catch regressions. Human specialists find the novel, multi-step and domain-specific attacks automation misses. We run both, and every human finding becomes an automated test.

What do we receive at the end of an engagement?

A threat model, a findings report with reproducible transcripts and severity ratings, recommended mitigations, a retest after fixes, and an eval suite that encodes every finding so it can never silently return.

Who are your red teamers?

Security researchers with offensive backgrounds, plus domain specialists in biology, chemistry, cybersecurity, finance, law and medicine for capability and misuse testing. All work under NDA with background checks.

Can you test agents that use MCP servers and internal tools?

Yes. We test against a staging copy of your agent with real tool schemas, seeded test data and sandboxed side effects, including MCP servers, browser tools and code execution.

How long does an engagement take?

A focused pre-launch assessment takes two to three weeks. Continuous red teaming runs every night against staging, with a monthly human deep-dive.

Related: AI red teaming methodology · Prompt injection · Reinforced Guard

Next step

Have it attacked by us, not by someone else.

A 45-minute briefing with our evaluation team. Bring a use case; leave with a threat model, a draft eval plan and a sense of where you stand.