Pencheff combines deterministic scanners, AI-guided probes, curated payloads, external tools, and evidence normalization so every signal lands in one remediation workflow.
AI security
Threat modeling
STRIDE, DREAD, attack trees, abuse cases, and generated mitigations per engagement.
Findings, reports, dashboards, exports, integrations, and retests all read from the same normalized record.
Pencheff favors repeatable checks, then uses AI for triage, enrichment, orchestration, and remediation where it adds signal.
Coverage
What does Threat modeling test?
- STRIDE, DREAD, attack trees, abuse cases, and generated mitigations per engagement.
- This page is part of Capabilities under Prioritization.
- It links back into the broader from live exploits to source-code proof experience.
- OWASP LLM Top 10 coverage for prompt injection, sensitive information disclosure, supply chain, data leakage, plugins, agency, overreliance, and model theft.
- Jailbreak strategies, roleplay, encoding, payload splitting, multilingual variants, custom datasets, and judge-backed scoring.
- Agentic tests for tool authorization, memory poisoning, context exfiltration, planner hijacking, and unsafe side effects.
- Sentry runtime guardrails, HTTP sidecars, LiteLLM plugins, MCP middleware, PII, secrets, unsafe HTML, and tool authorization checks.
- AI governance mapping to OWASP LLM, MITRE ATLAS, NIST AI RMF, EU AI Act, ISO/IEC 42001, GDPR, and SOC 2.
Execution
How does Pencheff run this?
- Register an LLM endpoint, chatbot, model gateway, MCP host, or agent workflow.
- Choose built-in categories, datasets, guardrails, custom prompts, and optional judge settings.
- Run adversarial campaigns across prompt, tool, memory, retrieval, output, and policy paths.
- Classify failures by category, strategy, severity, transcript, token cost, and guardrail recommendation.
- Turn passing and failing prompts into regression suites for releases and model upgrades.
Evidence
What evidence does this produce?
- Prompt, response, tool call, policy decision, transcript, category, strategy, judge result, and confidence.
- Recommended guardrails with exact unsafe behavior, enforcement point, and regression prompt.
- Token usage, model/provider metadata, retry behavior, and cost-oriented observability.
- Governance mappings for AI risk, safety, privacy, and compliance programs.
Controls
How is this kept safe to run?
- Tests can be run through HTTP, chat-completions, LiteLLM, MCP, or custom adapters.
- Guardrail recommendations stay tied to the scan that exposed the failure.
- Agentic testing focuses on authorization, context boundaries, and side-effect control.
- Runtime policy checks can be placed before prompts, after responses, or around tools.
Documentation
Read the full reference.
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