Use case
AI red-teaming
Adversarially test deployed AI and model changes for prompt injection, disclosure, jailbreaks, control bypass and security regressions before those behaviors reach production.
The challenge
Where assumptions break down
Functional evaluation can show that an AI feature performs its intended task while missing how it behaves under hostile input. Prompt injection, hidden-instruction disclosure, sensitive-information leakage and jailbreaks can appear only when the system is deliberately manipulated. Model changes add another problem because a retrain or fine-tune can change security behavior without changing the surrounding application code.
How RedMaw approaches it
- 01RedMaw tests deployed AI endpoints and features with adversarial prompts and supported attack probes.
- 02It records prompt and response evidence when a security boundary fails.
- 03Model and endpoint posture checks add context around exposure and serving configuration.
- 04AI red-teaming can run in CI and model-release workflows so candidate changes are tested before promotion.
- 05A candidate model can be compared with the last accepted security baseline and blocked when a material regression appears.
- 06Deeper AI-agent red-teaming and tool-abuse validation are planned. Broad model-poisoning detection is not claimed.
Outcome
What changes
Keep going
Related
Capabilities
- AI SecurityAdversarial testing of deployed AI systems and release pipelines, aimed at how the system behaves when the input is hostile rather than expected.
- Application SecurityAdversarial testing of the web applications and APIs you own and authorize, aimed at proving exploitability rather than reporting resemblance to a known pattern.
Stop assuming you are secure. Prove it.
Continuously test what an attacker can actually reach across your applications, SaaS identities, internal infrastructure and AI systems.