testing-llm-guardrails
De la autor
Tests the runtime validation around model output - schema conformance, PII leakage, moderation, refusal behaviour, tool-call safety - and the adversarial suite that tries to defeat it: direct prompt injection, jailbreaks, and indirect injection through retrieved documents, tool results and user content. Use when an LLM feature reaches real users, when a model can call tools or read untrusted content, when an AI feature needs a security-style test suite, or when asked whether a chatbot or agent can be manipulated.
Instalare
npx skills add jaktestowac/awesome-copilot-for-testers --skill testing-llm-guardrails -a claude-code
Se instalează în .claude/skills/testing-llm-guardrails/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
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De ce 81
aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- LLM Security TestingPramodDutta/qaskillsSecurity testing for LLM-powered applications including prompt injection, jailbreak detection, data leakage prevention, and AI safety testing.Securitate★ 244
- ai-system-testingpetrkindlmann/qa-skillsTest AI/LLM features that ship in your product. Covers prompt regression testing, response quality evaluation, tool-call validation, hallucination and RAG grounding checks, nondeterministic-output strategies, red-team/safety scans, eval frameworks, and agent-as-target injection (indirect injection via tool output / RAG / scan reports, self-propagating payloads, data exfiltration via an agent) plus a bundled detector for untrusted content. Use when: "test our LLM feature," "prompt regression test," "eval framework," "hallucination test," "RAG grounding," "nondeterministic output," "AI feature testing," "red-team our chatbot," "indirect prompt injection," "agent reading untrusted tool output," "production AI quality." Not for: using AI to generate your own test code — use ai-test-generation. Not for: classifying CI failures with AI — use ai-bug-triage. Not for: EU AI Act / GDPR conformity of an AI feature — use compliance-testing. Not for: canary/flag rollout of an AI feature — use testing-in-production. RelateTestare★ 235
- prompt-injectionUnitOneAI/SecuritySkillsTests LLM applications for prompt injection vulnerabilities per OWASP LLM01:2025. Covers direct injection (user input manipulating model behavior) and indirect injection (external content containing hidden instructions). Auto-invoked when reviewing LLM applications that process external content, build RAG pipelines, or accept user input that reaches a language model. Produces a test report with categorized findings and defense recommendations.Securitate★ 97
- testing-llm-featuresjaktestowac/awesome-copilot-for-testersBuilds an offline eval suite for LLM-powered features: golden reference cases per prompt template, deterministic scoring where possible, a regression gate on prompt, model and retrieval changes, and CI wiring with promptfoo, Vitest or deepeval. Use when a product ships prompts, agents or RAG, when a model or prompt upgrade needs a regression check, when LLM output is currently verified by someone eyeballing it, or when asked how to test a feature whose output is non-deterministic.Testare★ 118
- ai-llm-security-review26zl/cybersec-toolkitUse for AI/LLM security assessments, prompt injection, RAG security, agent/tool permissioning, model supply chain, LLM red teaming, AI governance, eval design, data leakage, jailbreak testing, and secure AI application review.Securitate★ 87
- LLM Output TestingPramodDutta/qaskillsTesting LLM-powered features including output quality validation, hallucination detection, token usage monitoring, and prompt regression testing.Testare★ 244
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