
attack-path-analysis
Use when Codex is already in the attack-path-analysis phase of a security scan or the user explicitly asks to trace a security finding from source to sink and calibrate severity. Do not use as the primary trigger for full PR, commit, branch, patch, or repository scans.
- Installs
- 734
- Last 24h
- +8
- Trending rank
- #9695
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/openai/codex-security --skill attack-path-analysis- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads attack-path-analysis automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Security Attack Path Analysis
Determine whether each supplied finding has a supported attacker path, what impact it has, and whether it is reportable. Before creating or retaining files, read ../../references/artifact-storage.md.
Establish the attack path
For a scan phase, use its per-scan threat model and candidate set from ../../references/scan-artifacts.md. Preserve supplied input paths. If a required scan artifact is unavailable, report the missing input.
For standalone analysis, use the supplied finding, repository, threat model or security policy, and context. A per-scan artifact is not required. State material assumptions and ask for missing context only when it prevents a meaningful assessment.
- Establish the in-scope attacker, affected product surface, entry point, controlled input, security boundary, and sensitive operation. Trace the path from repository evidence.
- Identify relevant controls and the strongest counterevidence. Explain what they establish, which prerequisites remain, and whether the exact claimed boundary is crossed.
- Assess impact and likelihood from those facts, then apply references/severity-policy.md. Keep facts, severity calibration, and the final policy decision distinguishable in the result.
- Return an explicit decision for every eligible candidate, including ignored or deferred candidates. Do not replace a supported instance with a neighboring finding or lose its root-control location.
Use repository evidence and explicitly supplied context. Access the network only when the user has expressly authorized it; an offline scan never accesses the network. Do not invent unsupported attack chains. Missing deployment or public-ingress evidence can limit confidence without defeating an otherwise supported finding.
Scan integration
Standard scans and Deep Scan workers assess attack paths within their ordinary Standard scan workflow; neither invokes this separate phase skill.
Compact Workbench Diff
When a workbench-backed $security-diff-scan has a scanId:
- Load the per-scan threat model and all validated candidates with
list_codex_security_candidates({ scanId, cursor?, limit? }). - Analyze every
reportableordeferredvalidation candidate. Preserve candidate identity and existing discovery and validation fields. - Submit every decision together with
record_candidate_attack_paths({ scanId, attackPaths: [{ candidateId, attackPath }] }). SubmitattackPaths: []when no candidate enters this phase. The tool preserves the stored candidate order. - These nested decisions are the complete phase output. Do not create duplicate reports, receipts, or manual ledgers.
Other phase-based scans
Use the active workflow's report and receipt paths in ../../references/scan-artifacts.md. Every candidate entering this phase must have an explicit decision and a receipt with its ID, facts or exact proof gap, and report reference. Record ignored candidates before excluding them from the surviving finding set.
For repository-wide or scoped-path scans, include validation closure rows marked reportable or survives: yes, even if discovery did not assign a polished finding number. Preserve affected entrypoint/wrapper, root-control, sink, and concrete-implementation locations supplied by validation.
Output
For standalone analysis, follow the caller's format or return a concise assessment per finding. Scan phases use their output contract above. Include:
- title and supplied candidate, instance, and ledger identities;
- precise affected locations and attacker-to-impact steps;
- relevant facts from references/attack-path-facts.md;
- controls, counterevidence, prerequisites, and material proof gaps;
- impact, likelihood, calibrated severity, and final policy decision with their reasons.
Retain enough evidence to explain both surviving and suppressed findings. Apply the severity policy to the established facts rather than reopening discovery or substituting a different attack story.
FAQ
What does the attack-path-analysis skill do?
Use when Codex is already in the attack-path-analysis phase of a security scan or the user explicitly asks to trace a security finding from source to sink and calibrate severity. Do not use as the primary trigger for full PR, commit, branch, patch, or repository scans.
How do I install attack-path-analysis?
Run npx skills add https://github.com/openai/codex-security --skill attack-path-analysis in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/attack-path-analysis/ (personal) or .claude/skills/attack-path-analysis/ (project).
Which AI agents can use attack-path-analysis?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is attack-path-analysis safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains attack-path-analysis?
It is published by openai in openai/codex-security. It has 734 recorded installs.
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