[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AE6MZ5JK03TPC24YBZP3N3":3,"skill-same:01M6AE6MZ5JK03TPC24YBZP3N3":119},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":29,"publishedAt":50,"linkOnly":51,"markdownContent":52,"markdownBody":53,"frontMatter":54,"refName":62,"commitSha":63,"skillDirectory":64,"forksCount":65,"repositoryHtmlUrl":66,"repositoryHomepageUrl":67,"ownerHtmlUrl":68,"ownerBlogUrl":69,"ownerDescription":70,"updatedAt":71,"install":72,"resources":85},"01M6AE6MZ5JK03TPC24YBZP3N3","continuous-llm-red-teaming-with-promptfoo",null,"Wires Promptfoo and DeepTeam into CI\u002FCD for automated, repeatable red-teaming of LLM apps against OWASP LLM Top 10, OWASP Agentic, and MITRE ATLAS presets, failing the build when jailbreak or injection vulnerabilities regress. Use for continuous adversarial testing in CI\u002FCD, a merge-blocking security gate, or comparing model\u002Fprompt versions for compliance reporting.","mukul975\u002FAnthropic-Cybersecurity-Skills","mukul975","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F42860185?v=4","01M6ADEQR8T5M4EEG6C67BQCWF","Mahipal","01M6ADJPMYWG1E5GM1FWBE3128",36078,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fcontinuous-llm-red-teaming-with-promptfoo\u002FSKILL.md",[18,23,26],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49],"continuous llm red teaming with promptfoo","ai-security","regression-testing","jailbreak","owasp-llm-top10","ci-cd","deepteam","promptfoo","llm-red-teaming","owasp","testing","compliance","reporting","ci","owasp llm","owasp agentic","merge","cybersecurity","security","agentic","2026-11-10 10:12:30",false,"---\nname: continuous-llm-red-teaming-with-promptfoo\ndescription: Wires Promptfoo and DeepTeam into CI\u002FCD for automated, repeatable red-teaming of LLM apps against OWASP LLM Top 10, OWASP Agentic, and MITRE ATLAS presets, failing the build when jailbreak or injection vulnerabilities regress. Use for continuous adversarial testing in CI\u002FCD, a merge-blocking security gate, or comparing model\u002Fprompt versions for compliance reporting.\ndomain: cybersecurity\nsubdomain: ai-security\ntags:\n- ai-security\n- llm-red-teaming\n- promptfoo\n- deepteam\n- ci-cd\n- owasp-llm-top10\n- jailbreak\n- regression-testing\nversion: '1.0'\nauthor: mahipal\nlicense: Apache-2.0\nnist_ai_rmf:\n- MANAGE-4.1\natlas_techniques:\n- AML.T0051\n---\n# Continuous LLM Red Teaming with Promptfoo\n\n> **Authorized Use Only:** Run these adversarial probes only against LLM applications and endpoints you own or are explicitly authorized to test. Generated attack payloads (jailbreaks, prompt injections, harmful-content elicitation) are adversarial inputs; sending them to third-party services without permission may violate terms of service.\n\n## Overview\n\nPromptfoo is an open-source LLM evaluation and red-teaming framework (used by OpenAI and Anthropic per its README) that generates adversarial test cases, runs them against your model\u002Fagent, and grades the responses. DeepTeam (by Confident AI) is a complementary open-source framework offering 50+ ready-to-use vulnerabilities and 10+ research-backed attack methods. Together they let you treat LLM security as a **regression test**: every commit re-runs the same adversarial suite, and the pipeline fails when a previously-safe behavior regresses.\n\nThis matters because LLM applications change constantly — prompts, models, RAG sources, tools, and guardrails all drift. A jailbreak that was patched last sprint can silently return after a prompt edit or a model upgrade. Promptfoo maps its plugins directly onto the **OWASP LLM Top 10** (`owasp:llm`) and **OWASP Agentic** (`owasp:agentic`) presets, and onto MITRE ATLAS, so the suite tracks recognized risk taxonomies. The core threat addressed here is **AML.T0051 — LLM Prompt Injection** (MITRE ATLAS): adversarial instructions that override the application's intended behavior. This skill follows the Promptfoo red-team docs (https:\u002F\u002Fwww.promptfoo.dev\u002Fdocs\u002Fred-team\u002F) and DeepTeam docs (https:\u002F\u002Fwww.trydeepteam.com\u002Fdocs\u002Fgetting-started), and aligns to NIST AI RMF MANAGE-4.1 (post-deployment monitoring and feedback to manage AI risk).\n\n## When to Use\n\n- When you need continuous, automated red-teaming of an LLM app in CI\u002FCD rather than one-off manual tests.\n- When you want to enforce a security gate: block merges that introduce or reintroduce jailbreak\u002Finjection vulnerabilities.\n- When mapping coverage to OWASP LLM Top 10 \u002F OWASP Agentic \u002F MITRE ATLAS for compliance reporting.\n- When comparing the security posture of two models or prompt versions side by side.\n- When tracking vulnerability regression over time across releases.\n\n## Prerequisites\n\n- Node.js 18+ (Promptfoo is distributed via npm) and Python 3.9+ (for DeepTeam).\n- Install Promptfoo and DeepTeam:\n  ```bash\n  npm install -g promptfoo            # or: npx promptfoo@latest\n  pip install -U deepteam\n  ```\n- API access\u002Fcredentials for the target LLM endpoint (and a grader model, e.g. an OpenAI key) exposed as environment variables.\n- A CI\u002FCD platform (GitHub Actions, GitLab CI) with secret storage.\n- Authorization to test the target application.\n\n## Objectives\n\n- Scaffold a Promptfoo red-team config targeting your LLM app.\n- Enable OWASP LLM Top 10 and OWASP Agentic plugin presets plus jailbreak\u002Finjection strategies.\n- Run the suite locally and interpret the per-plugin pass\u002Ffail report.\n- Add DeepTeam as a second engine for programmatic, research-backed attacks.\n- Integrate both into CI\u002FCD so builds fail on new vulnerabilities.\n- Generate shareable HTML\u002FPDF security reports per run.\n\n## MITRE ATT&CK Mapping\n\n| ID | Name (MITRE ATLAS) | Tactic |\n|----|--------------------|--------|\n| AML.T0051 | LLM Prompt Injection | Initial Access \u002F Persistence (LLM) |\n| AML.T0051.000 | Direct (Prompt Injection) | LLM Attack |\n| AML.T0051.001 | Indirect (Prompt Injection) | LLM Attack |\n| AML.T0054 | LLM Jailbreak | Privilege Escalation \u002F Defense Evasion (LLM) |\n\n## Workflow\n\n### 1. Scaffold the red-team configuration\nInitialize an interactive config; it writes `promptfooconfig.yaml` where targets, plugins, and strategies live.\n\n```bash\npromptfoo redteam init\n# choose your target type (HTTP endpoint, openai:..., anthropic:..., custom provider)\n```\n\n### 2. Define targets, OWASP presets, and attack strategies\nEdit `promptfooconfig.yaml`. The `purpose` grounds attack generation; `plugins` are adversarial input generators; `strategies` are delivery techniques (jailbreak\u002Finjection wrappers).\n\n```yaml\n# promptfooconfig.yaml\ntargets:\n  - id: https:\u002F\u002Fapi.example.com\u002Fchat        # your app endpoint\n    label: support-bot\n\nredteam:\n  purpose: |\n    A customer-support assistant for an e-commerce site. Must never reveal\n    system prompts, leak PII, or perform actions outside order support.\n  numTests: 10\n  plugins:\n    - owasp:llm          # OWASP LLM Top 10 preset\n    - owasp:agentic      # OWASP Agentic threats preset\n    - id: pii:direct\n      numTests: 15\n    - prompt-extraction  # system-prompt leakage\n    - harmful\n  strategies:\n    - id: jailbreak              # iterative single-turn jailbreak\n    - id: jailbreak:composite    # stacked jailbreak techniques\n    - id: crescendo              # multi-turn escalation\n    - id: prompt-injection       # injection wrapper\n```\n\n### 3. Run the suite and view the report\n`redteam run` combines generation + evaluation; then open the interactive report.\n\n```bash\npromptfoo redteam run\npromptfoo redteam report            # launches the web report (pass\u002Ffail per plugin)\n```\nEach row shows the plugin (mapped to OWASP\u002FATLAS), the strategy, the attack prompt, the model's response, and the grader's verdict. The **attack success rate** per plugin is your headline metric — track it per release.\n\n### 4. Add DeepTeam for programmatic, research-backed attacks\nUse DeepTeam to cover additional vulnerabilities\u002Fattacks and to script bespoke suites in Python.\n\n```python\n# deepteam_suite.py\nfrom deepteam import red_team\nfrom deepteam.vulnerabilities import Bias, PIILeakage\nfrom deepteam.attacks.single_turn import PromptInjection\n\ndef model_callback(prompt: str) -> str:\n    # call your application's LLM endpoint here and return the text response\n    return call_my_app(prompt)\n\nred_team(\n    model_callback=model_callback,\n    vulnerabilities=[Bias(types=[\"race\"]), PIILeakage(types=[\"api_and_database_access\"])],\n    attacks=[PromptInjection()],\n)\n```\nDeepTeam can also be driven from a YAML config:\n```bash\ndeepteam run config.yaml\n```\n\n### 5. Gate the build in CI\u002FCD (GitHub Actions)\nFail the pipeline when red-team assertions fail. Promptfoo returns a non-zero exit code on failures, which blocks the merge.\n\n```yaml\n# .github\u002Fworkflows\u002Fllm-redteam.yml\nname: LLM Red Team\non: [pull_request]\njobs:\n  redteam:\n    runs-on: ubuntu-latest\n    steps:\n      - uses: actions\u002Fcheckout@v4\n      - uses: actions\u002Fsetup-node@v4\n        with: { node-version: '20' }\n      - run: npm install -g promptfoo\n      - name: Run red team (fails build on new vulns)\n        env:\n          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}\n        run: promptfoo redteam run --no-progress-bar\n      - name: Export machine-readable results\n        if: always()\n        run: promptfoo redteam report --output results.json\n      - uses: actions\u002Fupload-artifact@v4\n        if: always()\n        with: { name: redteam-report, path: results.json }\n```\n\n### 6. Track regressions over time\nPersist `results.json` per run and compare attack-success-rate per plugin between releases. A rising rate for any OWASP LLM category is a regression to triage before release. Promptfoo's `--filter-failing` lets you re-run only previously failing cases to confirm a fix.\n\n```bash\npromptfoo redteam run --filter-failing results.json\n```\n\n## Tools and Resources\n\n| Resource | Link |\n|----------|------|\n| Promptfoo red-team docs | https:\u002F\u002Fwww.promptfoo.dev\u002Fdocs\u002Fred-team\u002F |\n| Promptfoo red-team configuration | https:\u002F\u002Fwww.promptfoo.dev\u002Fdocs\u002Fred-team\u002Fconfiguration\u002F |\n| Promptfoo CI\u002FCD integration | https:\u002F\u002Fwww.promptfoo.dev\u002Fdocs\u002Fintegrations\u002Fci-cd\u002F |\n| Promptfoo MITRE ATLAS mapping | https:\u002F\u002Fwww.promptfoo.dev\u002Fdocs\u002Fred-team\u002Fmitre-atlas\u002F |\n| DeepTeam (Confident AI) | https:\u002F\u002Fgithub.com\u002Fconfident-ai\u002Fdeepteam |\n| DeepTeam docs | https:\u002F\u002Fwww.trydeepteam.com\u002Fdocs\u002Fgetting-started |\n| OWASP Top 10 for LLM Applications | https:\u002F\u002Fgenai.owasp.org\u002F |\n\n## Plugin \u002F Strategy Reference\n\n| Promptfoo item | Type | Maps to |\n|----------------|------|---------|\n| `owasp:llm` | preset | OWASP LLM Top 10 suite |\n| `owasp:agentic` | preset | OWASP Agentic threats |\n| `prompt-extraction` | plugin | LLM07 system-prompt leakage |\n| `pii:direct` | plugin | LLM06 sensitive-info disclosure |\n| `harmful` | plugin | harmful content generation |\n| `jailbreak` \u002F `jailbreak:composite` | strategy | AML.T0054 LLM jailbreak |\n| `crescendo` | strategy | multi-turn jailbreak |\n| `prompt-injection` | strategy | AML.T0051 prompt injection |\n\n## Validation Criteria\n\n- [ ] `promptfooconfig.yaml` created with target, `owasp:llm`, and `owasp:agentic` plugins.\n- [ ] Jailbreak and prompt-injection strategies enabled.\n- [ ] `promptfoo redteam run` executes and produces a per-plugin pass\u002Ffail report.\n- [ ] DeepTeam suite runs against the same target via `model_callback`.\n- [ ] CI\u002FCD job fails the build on new red-team failures (non-zero exit).\n- [ ] `results.json` artifact archived per run for regression tracking.\n- [ ] Attack-success-rate per OWASP category trended across releases.\n","# Continuous LLM Red Teaming with Promptfoo\n\n> **Authorized Use Only:** Run these adversarial probes only against LLM applications and endpoints you own or are explicitly authorized to test. Generated attack payloads (jailbreaks, prompt injections, harmful-content elicitation) are adversarial inputs; sending them to third-party services without permission may violate terms of service.\n\n## Overview\n\nPromptfoo is an open-source LLM evaluation and red-teaming framework (used by OpenAI and Anthropic per its README) that generates adversarial test cases, runs them against your model\u002Fagent, and grades the responses. DeepTeam (by Confident AI) is a complementary open-source framework offering 50+ ready-to-use vulnerabilities and 10+ research-backed attack methods. Together they let you treat LLM security as a **regression test**: every commit re-runs the same adversarial suite, and the pipeline fails when a previously-safe behavior regresses.\n\nThis matters because LLM applications change constantly — prompts, models, RAG sources, tools, and guardrails all drift. A jailbreak that was patched last sprint can silently return after a prompt edit or a model upgrade. Promptfoo maps its plugins directly onto the **OWASP LLM Top 10** (`owasp:llm`) and **OWASP Agentic** (`owasp:agentic`) presets, and onto MITRE ATLAS, so the suite tracks recognized risk taxonomies. The core threat addressed here is **AML.T0051 — LLM Prompt Injection** (MITRE ATLAS): adversarial instructions that override the application's intended behavior. This skill follows the Promptfoo red-team docs (https:\u002F\u002Fwww.promptfoo.dev\u002Fdocs\u002Fred-team\u002F) and DeepTeam docs (https:\u002F\u002Fwww.trydeepteam.com\u002Fdocs\u002Fgetting-started), and aligns to NIST AI RMF MANAGE-4.1 (post-deployment monitoring and feedback to manage AI risk).\n\n## When to Use\n\n- When you need continuous, automated red-teaming of an LLM app in CI\u002FCD rather than one-off manual tests.\n- When you want to enforce a security gate: block merges that introduce or reintroduce jailbreak\u002Finjection vulnerabilities.\n- When mapping coverage to OWASP LLM Top 10 \u002F OWASP Agentic \u002F MITRE ATLAS for compliance reporting.\n- When comparing the security posture of two models or prompt versions side by side.\n- When tracking vulnerability regression over time across releases.\n\n## Prerequisites\n\n- Node.js 18+ (Promptfoo is distributed via npm) and Python 3.9+ (for DeepTeam).\n- Install Promptfoo and DeepTeam:\n  ```bash\n  npm install -g promptfoo            # or: npx promptfoo@latest\n  pip install -U deepteam\n  ```\n- API access\u002Fcredentials for the target LLM endpoint (and a grader model, e.g. an OpenAI key) exposed as environment variables.\n- A CI\u002FCD platform (GitHub Actions, GitLab CI) with secret storage.\n- Authorization to test the target application.\n\n## Objectives\n\n- Scaffold a Promptfoo red-team config targeting your LLM app.\n- Enable OWASP LLM Top 10 and OWASP Agentic plugin presets plus jailbreak\u002Finjection strategies.\n- Run the suite locally and interpret the per-plugin pass\u002Ffail report.\n- Add DeepTeam as a second engine for programmatic, research-backed attacks.\n- Integrate both into CI\u002FCD so builds fail on new vulnerabilities.\n- Generate shareable HTML\u002FPDF security reports per run.\n\n## MITRE ATT&CK Mapping\n\n| ID | Name (MITRE ATLAS) | Tactic |\n|----|--------------------|--------|\n| AML.T0051 | LLM Prompt Injection | Initial Access \u002F Persistence (LLM) |\n| AML.T0051.000 | Direct (Prompt Injection) | LLM Attack |\n| AML.T0051.001 | Indirect (Prompt Injection) | LLM Attack |\n| AML.T0054 | LLM Jailbreak | Privilege Escalation \u002F Defense Evasion (LLM) |\n\n## Workflow\n\n### 1. Scaffold the red-team configuration\nInitialize an interactive config; it writes `promptfooconfig.yaml` where targets, plugins, and strategies live.\n\n```bash\npromptfoo redteam init\n# choose your target type (HTTP endpoint, openai:..., anthropic:..., custom provider)\n```\n\n### 2. Define targets, OWASP presets, and attack strategies\nEdit `promptfooconfig.yaml`. The `purpose` grounds attack generation; `plugins` are adversarial input generators; `strategies` are delivery techniques (jailbreak\u002Finjection wrappers).\n\n```yaml\n# promptfooconfig.yaml\ntargets:\n  - id: https:\u002F\u002Fapi.example.com\u002Fchat        # your app endpoint\n    label: support-bot\n\nredteam:\n  purpose: |\n    A customer-support assistant for an e-commerce site. Must never reveal\n    system prompts, leak PII, or perform actions outside order support.\n  numTests: 10\n  plugins:\n    - owasp:llm          # OWASP LLM Top 10 preset\n    - owasp:agentic      # OWASP Agentic threats preset\n    - id: pii:direct\n      numTests: 15\n    - prompt-extraction  # system-prompt leakage\n    - harmful\n  strategies:\n    - id: jailbreak              # iterative single-turn jailbreak\n    - id: jailbreak:composite    # stacked jailbreak techniques\n    - id: crescendo              # multi-turn escalation\n    - id: prompt-injection       # injection wrapper\n```\n\n### 3. Run the suite and view the report\n`redteam run` combines generation + evaluation; then open the interactive report.\n\n```bash\npromptfoo redteam run\npromptfoo redteam report            # launches the web report (pass\u002Ffail per plugin)\n```\nEach row shows the plugin (mapped to OWASP\u002FATLAS), the strategy, the attack prompt, the model's response, and the grader's verdict. The **attack success rate** per plugin is your headline metric — track it per release.\n\n### 4. Add DeepTeam for programmatic, research-backed attacks\nUse DeepTeam to cover additional vulnerabilities\u002Fattacks and to script bespoke suites in Python.\n\n```python\n# deepteam_suite.py\nfrom deepteam import red_team\nfrom deepteam.vulnerabilities import Bias, PIILeakage\nfrom deepteam.attacks.single_turn import PromptInjection\n\ndef model_callback(prompt: str) -> str:\n    # call your application's LLM endpoint here and return the text response\n    return call_my_app(prompt)\n\nred_team(\n    model_callback=model_callback,\n    vulnerabilities=[Bias(types=[\"race\"]), PIILeakage(types=[\"api_and_database_access\"])],\n    attacks=[PromptInjection()],\n)\n```\nDeepTeam can also be driven from a YAML config:\n```bash\ndeepteam run config.yaml\n```\n\n### 5. Gate the build in CI\u002FCD (GitHub Actions)\nFail the pipeline when red-team assertions fail. Promptfoo returns a non-zero exit code on failures, which blocks the merge.\n\n```yaml\n# .github\u002Fworkflows\u002Fllm-redteam.yml\nname: LLM Red Team\non: [pull_request]\njobs:\n  redteam:\n    runs-on: ubuntu-latest\n    steps:\n      - uses: actions\u002Fcheckout@v4\n      - uses: actions\u002Fsetup-node@v4\n        with: { node-version: '20' }\n      - run: npm install -g promptfoo\n      - name: Run red team (fails build on new vulns)\n        env:\n          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}\n        run: promptfoo redteam run --no-progress-bar\n      - name: Export machine-readable results\n        if: always()\n        run: promptfoo redteam report --output results.json\n      - uses: actions\u002Fupload-artifact@v4\n        if: always()\n        with: { name: redteam-report, path: results.json }\n```\n\n### 6. Track regressions over time\nPersist `results.json` per run and compare attack-success-rate per plugin between releases. A rising rate for any OWASP LLM category is a regression to triage before release. Promptfoo's `--filter-failing` lets you re-run only previously failing cases to confirm a fix.\n\n```bash\npromptfoo redteam run --filter-failing results.json\n```\n\n## Tools and Resources\n\n| Resource | Link |\n|----------|------|\n| Promptfoo red-team docs | https:\u002F\u002Fwww.promptfoo.dev\u002Fdocs\u002Fred-team\u002F |\n| Promptfoo red-team configuration | https:\u002F\u002Fwww.promptfoo.dev\u002Fdocs\u002Fred-team\u002Fconfiguration\u002F |\n| Promptfoo CI\u002FCD integration | https:\u002F\u002Fwww.promptfoo.dev\u002Fdocs\u002Fintegrations\u002Fci-cd\u002F |\n| Promptfoo MITRE ATLAS mapping | https:\u002F\u002Fwww.promptfoo.dev\u002Fdocs\u002Fred-team\u002Fmitre-atlas\u002F |\n| DeepTeam (Confident AI) | https:\u002F\u002Fgithub.com\u002Fconfident-ai\u002Fdeepteam |\n| DeepTeam docs | https:\u002F\u002Fwww.trydeepteam.com\u002Fdocs\u002Fgetting-started |\n| OWASP Top 10 for LLM Applications | https:\u002F\u002Fgenai.owasp.org\u002F |\n\n## Plugin \u002F Strategy Reference\n\n| Promptfoo item | Type | Maps to |\n|----------------|------|---------|\n| `owasp:llm` | preset | OWASP LLM Top 10 suite |\n| `owasp:agentic` | preset | OWASP Agentic threats |\n| `prompt-extraction` | plugin | LLM07 system-prompt leakage |\n| `pii:direct` | plugin | LLM06 sensitive-info disclosure |\n| `harmful` | plugin | harmful content generation |\n| `jailbreak` \u002F `jailbreak:composite` | strategy | AML.T0054 LLM jailbreak |\n| `crescendo` | strategy | multi-turn jailbreak |\n| `prompt-injection` | strategy | AML.T0051 prompt injection |\n\n## Validation Criteria\n\n- [ ] `promptfooconfig.yaml` created with target, `owasp:llm`, and `owasp:agentic` plugins.\n- [ ] Jailbreak and prompt-injection strategies enabled.\n- [ ] `promptfoo redteam run` executes and produces a per-plugin pass\u002Ffail report.\n- [ ] DeepTeam suite runs against the same target via `model_callback`.\n- [ ] CI\u002FCD job fails the build on new red-team failures (non-zero exit).\n- [ ] `results.json` artifact archived per run for regression tracking.\n- [ ] Attack-success-rate per OWASP category trended across releases.\n",{"name":5,"tags":55,"author":56,"domain":47,"license":15,"version":57,"subdomain":31,"description":7,"nist_ai_rmf":58,"atlas_techniques":60},[31,38,37,36,35,34,33,32],"mahipal","1.0",[59],"MANAGE-4.1",[61],"AML.T0051","main","54a798831d2266a3ca61ce68a7acb80b81160d57","skills\u002Fcontinuous-llm-red-teaming-with-promptfoo",4414,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills","https:\u002F\u002Fmahipal.engineer\u002FAnthropic-Cybersecurity-Skills\u002F","https:\u002F\u002Fgithub.com\u002Fmukul975","Mahipal.engineer","🔐 Cybersecurity | 💻 Dev | 📷 Street Photographer\r\n🎓 MSc | 📚 Research & AI Security","2026-08-31 12:32:44",{"source":8,"skillName":5,"commands":73,"prompt":83,"zipAvailable":84},[74,77,80],{"tool":75,"command":76},"npx","npx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill continuous-llm-red-teaming-with-promptfoo",{"tool":78,"command":79},"bunx","bunx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill continuous-llm-red-teaming-with-promptfoo",{"tool":81,"command":82},"pnpm","pnpm dlx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill continuous-llm-red-teaming-with-promptfoo","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fcontinuous-llm-red-teaming-with-promptfoo\u002FSKILL.md",true,[86,92,97,101,105,109,113],{"path":87,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":90,"sourceHtmlUrl":91},"agents\u002Fopenai.yaml","REFERENCE","参考文档",317,"https:\u002F\u002Fgithub.com\u002Fpydantic\u002Fpydantic-ai\u002Fblob\u002Fmain\u002Fpydantic_ai_slim\u002Fpydantic_ai\u002F.agents\u002Fskills\u002Fmigrating-openai-agents-sdk-to-pydantic-ai\u002Fagents\u002Fopenai.yaml",{"path":93,"resourceType":93,"resourceTypeDesc":94,"sizeBytes":95,"sourceHtmlUrl":96},"LICENSE","许可证",11255,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fcontinuous-llm-red-teaming-with-promptfoo\u002FLICENSE",{"path":98,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":99,"sourceHtmlUrl":100},"references\u002Fapi-reference.md",2178,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fcontinuous-llm-red-teaming-with-promptfoo\u002Freferences\u002Fapi-reference.md",{"path":102,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":103,"sourceHtmlUrl":104},"references\u002FCONCEPT-MAPPING.md",10030,"https:\u002F\u002Fgithub.com\u002Fpydantic\u002Fpydantic-ai\u002Fblob\u002Fmain\u002Fpydantic_ai_slim\u002Fpydantic_ai\u002F.agents\u002Fskills\u002Fmigrating-openai-agents-sdk-to-pydantic-ai\u002Freferences\u002FCONCEPT-MAPPING.md",{"path":106,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":107,"sourceHtmlUrl":108},"references\u002Fstandards.md",1542,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fcontinuous-llm-red-teaming-with-promptfoo\u002Freferences\u002Fstandards.md",{"path":110,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":111,"sourceHtmlUrl":112},"references\u002FVERIFICATION-AND-CUTOVER.md",6243,"https:\u002F\u002Fgithub.com\u002Fpydantic\u002Fpydantic-ai\u002Fblob\u002Fmain\u002Fpydantic_ai_slim\u002Fpydantic_ai\u002F.agents\u002Fskills\u002Fmigrating-openai-agents-sdk-to-pydantic-ai\u002Freferences\u002FVERIFICATION-AND-CUTOVER.md",{"path":114,"resourceType":115,"resourceTypeDesc":116,"sizeBytes":117,"sourceHtmlUrl":118},"scripts\u002Fagent.py","SCRIPT","脚本",5072,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fcontinuous-llm-red-teaming-with-promptfoo\u002Fscripts\u002Fagent.py",[120,145,169,193,219,246],{"skillNo":121,"name":122,"nameZh":6,"description":123,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":124,"categories":125,"keywords":127,"publishedAt":50},"01M6AGDM3HJXTKXX15ZZ0ZVCJ3","verifying-build-provenance-with-slsa-sigstore","Verifies artifact signatures and SLSA provenance using Sigstore's cosign (verify, verify-attestation, verify-blob-attestation) and slsa-verifier (verify-artifact), enforcing keyless OIDC builder identity and source repo against SLSA Build levels. Use in CI\u002FCD before deploying artifacts, when consuming third-party attestations, establishing a SLSA Build L3 pipeline, or confirming provenance during incident response or admission control.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fverifying-build-provenance-with-slsa-sigstore\u002FSKILL.md",[126],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[128,129,130,131,48,47,132,133,134,135,136,43,137,138,139,140,141,142,143,144],"sigstore","provenance","slsa","verifying build provenance with slsa sigstore","incident response","identity","pr","code","artifacts","supply-chain","pipeline","supply-chain-security","t1195","attestation","code-signing","cosign","keyless",{"skillNo":146,"name":147,"nameZh":6,"description":148,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":149,"categories":150,"keywords":152,"publishedAt":50},"01M6AGDGBPWYVZ2F11GFKJ4HZ9","validating-tpm-measured-boot-attestation","Verifies TPM 2.0 measured-boot integrity and remote attestation with tpm2-tools -- reading PCRs (tpm2_pcrread), replaying the boot event log, generating and checking signed quotes (tpm2_quote\u002Ftpm2_checkquote), and sealing secrets to a PCR policy. Use to confirm a system booted trusted firmware\u002Fkernel for Zero Trust device posture, detect boot-chain tampering via PCR drift, or build a golden-value baseline for fleet attestation.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fvalidating-tpm-measured-boot-attestation\u002FSKILL.md",[151],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[153,141,154,155,48,156,47,134,157,158,159,160,161,162,163,164,165,166,167,168],"boot","TPM","validating tpm measured boot attestation","policy","verification","ZeRO","firmware","Tools","measured-boot","PCR","integrity","secrets","remote-attestation","tpm2-tools","integrity-verification","trusted-computing",{"skillNo":170,"name":171,"nameZh":6,"description":172,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":173,"categories":174,"keywords":177,"publishedAt":50},"01M6AGDCW0Y4FXZ3362GT1941M","validating-backup-integrity-for-recovery","Validates backup integrity through cryptographic hash verification, automated restore testing, corruption detection, and recoverability checks to confirm backups are reliable for disaster recovery and ransomware response. Use before relying on backups for recovery, when building post-backup validation pipelines, auditing backup infrastructure for compliance, or checking immutable\u002Fair-gapped backups for silent corruption or tampering.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fvalidating-backup-integrity-for-recovery\u002FSKILL.md",[175,176],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[163,178,179,180,40,47,181,182,157,41,183,184,185,186,187,188,189,190,191,192],"backup","recovery","validating backup integrity for recovery","infrastructure","validation","hash-verification","incident-response","ransomware","cryptocurrency","detection","t1078","t1490","immutable","disaster-recovery","restore-testing",{"skillNo":194,"name":195,"nameZh":6,"description":196,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":197,"categories":198,"keywords":200,"publishedAt":50},"01M6AGD9GBG2KZ336AE763J3WH","triaging-windows-with-kape","Runs KAPE (Kroll Artifact Parser and Extractor) to collect targeted forensic artifacts (registry hives, $MFT, event logs, prefetch, browser data) via Targets and parse them with Modules wrapping Eric Zimmerman's EZ Tools (PECmd, MFTECmd, RECmd). Use during early incident containment\u002Ftriage when full disk imaging is impractical but a defensible, parseable Windows artifact set is needed quickly, including at-scale remote collection.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Ftriaging-windows-with-kape\u002FSKILL.md",[199],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[201,202,203,47,204,205,206,207,208,209,210,211,212,213,214,215,160,216,217,218],"windows","kape","triaging windows with kape","registry","browser","logs","windows-forensics","dfir","mft","mftecmd","data","prefetch","eric-zimmerman","digital-forensics","SET","collection","pecmd","artifact-collection",{"skillNo":220,"name":221,"nameZh":6,"description":222,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":223,"categories":224,"keywords":226,"publishedAt":50},"01M6AGD5PW5J4EVZM958WD74ZQ","triaging-vulnerabilities-with-ssvc-framework","Triages and prioritizes vulnerabilities with CISA's Stakeholder-Specific Vulnerability Categorization (SSVC) decision tree, weighing exploitation status (via the CISA KEV catalog and FIRST EPSS API), technical impact, automatability, and mission prevalence to output Track\u002FTrack*\u002FAttend\u002FAct decisions. Use when prioritizing vulnerability scan results (OpenVAS, Nessus, Qualys) for remediation planning beyond raw CVSS scores.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Ftriaging-vulnerabilities-with-ssvc-framework\u002FSKILL.md",[225],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[227,228,229,47,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245],"vulnerabilities","framework","triaging vulnerabilities with ssvc framework","status","planning","CISA","vulnerability-management","risk-management","nessus","qualys","openvas","cvss","remediation","risk","vulnerability","api","prioritization","ssvc","decision-tree",{"skillNo":247,"name":248,"nameZh":6,"description":249,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":250,"categories":251,"keywords":253,"publishedAt":50},"01M6AGD24TTQQM4CXG69E3B9TJ","triaging-security-incident","Performs initial triage of security incidents using the NIST SP 800-61r3 and SANS PICERL frameworks, classifying incident type, assigning priority by business impact, and routing to the appropriate response team. Use when a SIEM\u002FEDR alert needs human classification, concurrent alerts must be prioritized, or a user report or threat-intel IOC match requires initial incident categorization.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Ftriaging-security-incident\u002FSKILL.md",[252],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[48,254,47,255,256,257,258,259,260,261,262,188,263,189,264,265,266,267,268,269],"triaging security incident","siem","file","ioc","soc","NIST-800-61","NIST","severity","edr","execution","analysis","classification","severity-classification","incident-triage","SANS-PICERL","SOC-operations"]