Manual AI red teaming is slow, expensive, and happens once a quarter at best. Kovral automates the process — running adversarial scenarios against your AI applications continuously, the same way CI/CD runs tests against your code.
Jailbreaks, prompt injections, data extraction, role-play exploits, multi-step reasoning attacks, indirect injection, encoding evasion — all executed automatically against your AI endpoints.
Red team tests run on a configurable schedule (hourly, daily, or triggered by model updates). New attack signatures are added to the library within 24 hours of public disclosure.
Each test run produces a detailed vulnerability report: attack vector, success/failure, risk severity, OWASP LLM classification, and remediation recommendation.
Integrate red team tests into your CI/CD pipeline as a deployment gate. Block deployments where new model versions introduce vulnerabilities that didn't exist in the previous version.
Our attack library covers all categories in the OWASP Top 10 for LLMs and extends into proprietary research threat classes.
Direct injection, indirect injection via tool results, system prompt extraction, and instruction override attempts across 120+ scenario variants.
Training data extraction, membership inference, system prompt leakage, and cross-user data leakage across 80+ scenario variants.
XSS via model output, code injection through generated code, and command injection via model-controlled tool calls.
Agent goal drift, unauthorised tool use, privilege escalation via tool chaining, and scope creep in autonomous workflows.
Direct extraction attempts, indirect inference via carefully crafted questions, and side-channel approaches to reconstruct system prompt content.
Token exhaustion, recursive generation loops, context window flooding, and compute amplification attacks.
