Complete AI Stack View
End-to-end coverage across prompts, RAG, agents/tools, data connectors, and classic ML pipelines—prioritized by business risk.
Traditional testing misses AI-specific failure modes—prompt injection, jailbreaks, tool-call abuse, data exfiltration via RAG, model theft, membership inference, and poisoning. Forgepath brings structured adversarial methods to your AI stack: we map your model routes and tools, craft context-aware attacks, and confirm what’s actually exploitable under real policies and guardrails.
We focus on where impact is highest: system prompts and safety rules, retrieval pipelines and embeddings, tool permissioning, data connectors, and output post-processing. You’ll get reproducible findings with attack transcripts, payloads, and mitigations (policy updates, filters, gating, and architectural patterns).
End-to-end coverage across prompts, RAG, agents/tools, data connectors, and classic ML pipelines—prioritized by business risk.
Findings reflect realistic attack chains (prompt injection, tool abuse, leakage), not theoretical lists.
Attack transcripts, payloads, and replay steps mapped to impact and remediation.
Engineer-ready mitigations (policy updates, filters, scopes) and an included re-test to confirm closure.
Guardrails that shrink PII/IP exposure and prevent risky tool actions.
Monitoring hooks and safety KPIs that keep improvements in place across releases.