菜单
Look up and map to the right OWASP Top 10 family — Web, API, LLM, or Mobile — and give the canonical category ID/name for a finding. Use when you need a consistent OWASP reference for tagging findings, scoping a review, or aligning a report across the appsec/genai plugins.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Statically review a Claude Code / AI-agent setup for security misconfigurations — risky hooks, over-broad permissions, untrusted or cleartext MCP servers, hardcoded secrets, endpoint redirection, over-privileged agents/skills, and prompt-injection in steering files. Use when asked to audit a `.claude/` directory, `settings.json`, `.mcp.json`, a plugin/marketplace, or `CLAUDE.md`, or to add a config-security gate to CI. Drives the `agentscanner` CLI.
Review the training / MLOps pipeline for security weaknesses — data-poisoning surface, feature-store and data-source trust, experiment-tracking and model-registry access control, secrets, and reproducibility/provenance. Use when assessing how models are built and promoted, not how they're served or consumed.
Review the provenance and integrity of models, datasets, and ML artifacts pulled from hubs/registries — unsafe deserialization (pickle, PyTorch/Keras/joblib), untrusted model sources, model/dataset tampering, and signing. Use when an app loads third-party model weights or datasets, or before promoting a model.
Harden a deployed model inference endpoint — authn/authz, rate limiting and abuse control, input validation, and exposure to model-extraction / inversion / membership-inference attacks. Use when reviewing how a model is served (REST/gRPC, Triton/TorchServe/KServe, or a hosted inference API), not how it was trained.
Export a finished security artifact (report, executive summary, diagram, spreadsheet of findings) to Google Drive — right folder, clear naming, and shareable with the intended audience. Use when a deliverable needs to be handed to stakeholders who live in Drive/Docs/Sheets rather than a wiki or tracker.
Turn a security finding (vuln, pentest issue, review item) into a well-formed Jira issue — mapped severity→priority, remediation/repro in the body, labels and components set, and dedup-checked against existing issues. Use when findings need to become tracked, assignable work in Jira.
| name | owasp-reference |
| description | Look up and map to the right OWASP Top 10 family — Web, API, LLM, or Mobile — and give the canonical category ID/name for a finding. Use when you need a consistent OWASP reference for tagging findings, scoping a review, or aligning a report across the appsec/genai plugins. |
The correct OWASP family and category for a finding or scope, cited consistently — so reviews and reports across web, API, LLM, and mobile work line up.
A01:2021 – Broken Access Control, API1:2023 – BOLA, LLM01 – Prompt Injection). See
reference.md for all four lists.web-app-security, api-security, llm-security, mobile-security);
this skill is the consistent reference/tagging layer.The family + category ID/name, a one-line rationale, and a pointer to the relevant testing skill. For multi-category findings, list each with the primary one first.
OWASP lists are versioned and renumber between editions — always cite the year/edition
(A01:2021, API1:2023, LLM01:2025). Don't force a finding into a category it doesn't
fit; "doesn't map cleanly" is a valid, useful answer. See reference.md for the full
category lists.