DriftSight

AI red-teaming is slow, manual, and expensive. DriftSight is a self-improving adversarial agent that automates it — attacking AI systems, learning from every failure, and getting smarter over time. Built for security engineers, AI teams, and compliance-focused organizations who need to know: does this model actually follow its own rules? Three attack modes: Autonomous Red-Team — ReconAgent maps known vulnerabilities via parallel web search. PlannerAgent builds a targeted attack plan. Concurrent Attacker→Executor→Judge triads probe the live model and score every response. Full executive report at the end — all streamed live to a Vercel dashboard via persistent SSE connections, so audits feel like a product, not a script. Policy Drift Auditing — Upload a system prompt or PDF. Gemini extracts every behavioral constraint into a structured baseline. DriftSight fires adaptive probes at the target model, semantically diffs each response against the policy, and produces a 0.0–1.0 drift score with verbatim violation evidence and an incident-style reasoning chain. Critically, the attacker changes strategy every iteration based on why the last probe failed — genuine agentic reasoning, not brute force. Visual Injection Detection — Gemini Vision scans uploaded images for steganographic text and embedded prompt injections in under 2 seconds. The differentiator: attack memory. Every successful attack is embedded with Gemini text-embedding-004 and stored in Supabase pgvector. New sessions retrieve semantically similar past successes and prime the attacker with winning prompts — DriftSight compounds its effectiveness across every audit run. Each pipeline runs in an isolated Daytona sandbox. Auth via Clerk. Gemini powers this end-to-end: policy extraction, adaptive adversarial reasoning, semantic drift scoring, and multimodal vision — all in one API. Vercel makes the live streaming pipeline production-grade.