evaluating-model-guardrails
SolidSystematically test whether a model's safety and policy guardrails hold under adversarial pressure, as a repeatable evaluation rather than a one-off trick. Covers building a policy-grounded probe set, multi-turn escalation, encoding and obfuscation bypasses, role and context reframing, instruction-hierarchy conflicts, and measuring bypass rate with reproducible graded outcomes. Use when reviewing a model deployment, a system prompt, or a content-safety layer, or before shipping an assistant that must refuse a defined set of requests. A rate, not an anecdote.
Install
Quality Score: 80/100
Skill Content
Details
- Author
- UnboundCompute
- Repository
- UnboundCompute/security-agent-skills
- Created
- 5 days ago
- Last Updated
- yesterday
- Language
- N/A
- License
- MIT
Similar Skills
Semantically similar based on skill content — not just same category
rag-eval-guardrails
Build a verified eval harness for a RAG/LLM feature plus PII/PHI-leakage guardrails, gated by checks that actually run. Scores a precomputed predictions file (so it runs with ZERO API access) on groundedness, citation validity, retrieval hit@k, answer F1/exact-match, refusal rate, and latency; compares to config thresholds and a baseline to catch regressions; and fails the build on PII/PHI leakage. Use when the user wants to evaluate or regression-test an AI/RAG feature, measure hallucination/groundedness, add an eval gate to CI, or scan prompts/answers/logs for leaked identifiers. Triggers: "RAG evaluation", "LLM eval", "eval harness", "hallucination", "groundedness", "PII/PHI leakage", "guardrails", "regression testing for AI features".
add-llm-guardrails
Use this to add safety and security guardrails to an LLM/agent app - blocking prompt injection, PII leakage, jailbreaks, toxic output, off-topic responses, or invalid structured output. Trigger on "add guardrails", "prevent prompt injection", "stop PII leaks", "validate the model's output", "make this safe for production", especially for regulated/finance/enterprise use.
model-eval
Deep security evaluation of an AI model artifact — a public HuggingFace model (especially custom-code / trust_remote_code models) OR a model folder already on disk. Runs a deterministic collector, authors a dual-pass behavioral claim and checks it against the code's actual call graph, traces generation-to-sink paths, and produces a plain-English report (decision on top, evidence below) with an audit-opinion verdict. Use when asked to evaluate, audit, vet, or decide whether to adopt a model that ships custom code, or after model-scorecard returns tier C or D. EXECUTES NOTHING from the artifact and NEVER issues a clearance; a code-execution artifact seen static-only receives a "disclaimer of opinion".