security-code-audit
Skill47 starsChanged 5 months agoMITView on GitHub
Use when you have application source code and want to find security vulnerabilities by reading it — covers OWASP Top 10 across frontend, backend API, and database. Produces a findings register with severity, file:line, and exploitability.
1 other project uses a version of this.
Adopted by
Most recently taken up by
- sari3l/security-code-audit-skilltheir own version, first seen Oct 27, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- sari3l/security-code-audit-skill★ 2same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository15 other files it gives its agents.
- access-control-testingSkill
- active-pentestSkill
- api-security-testingSkill
- authentication-testingSkill
- business-logic-testingSkill
- client-side-exploitationSkill
- file-upload-and-ssrfSkill
- injection-testingSkill
- pentest-reportingSkill
- recon-and-osintSkill
- secrets-management-auditSkill
- security-hardeningSkill
- security-testingSkill
- threat-modelingSkill
- vulnerability-chainingSkill
The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 3 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 9
Read source code and find vulnerabilities by pattern.
- Distinctive: line 18
Follow the data through the code to where it's used (the sink).
- Distinctive: line 19
Ask: is it validated, sanitized, parameterized, or encoded *for that sink*?
- Very specific: line 50
All findings → security-hardening to fix.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Security Code Audit (FIND phase)
Overview
Read source code and find vulnerabilities by pattern. No running app required — works in any AI coding tool. Trace untrusted input from its source (where it enters) to its sink (where it's used dangerously).
Core principle: A vulnerability is untrusted data reaching a dangerous sink without adequate validation/encoding in between. Find the source→sink paths.
Method: source → sink tracing
- From the threat model (or by scanning entry points), pick a source of untrusted input.
- Follow the data through the code to where it's used (the sink).
- Ask: is it validated, sanitized, parameterized, or encoded for that sink? If not → finding.
- Record severity,
file:line, the exact data path, and how to exploit it.
OWASP Top 10 — what to grep for
| Risk | Smell / sink to search |
| Injection (SQL/NoSQL/cmd) | string-built queries, exec, eval, system, template interpolation in queries |
| Broken access control (IDOR) | object lookups by user-supplied id with no ownership check |
| Broken auth | custom crypto, weak session handling, missing MFA, token in URL |
| Cryptographic failures | md5/sha1 for passwords, hardcoded keys, Math.random for tokens, no TLS |
| SSRF | server fetches a user-supplied URL (requests.get(input), fetch(input)) |
| Security misconfig | debug=true, default creds, permissive CORS *, verbose errors |
| XSS | innerHTML, dangerouslySetInnerHTML, v-html, unescaped template output |
| Insecure deserialization | pickle.loads, yaml.load, native deserialize on input |
| Vulnerable dependencies | check lockfiles vs advisories (npm audit, pip-audit, osv) |
| SSTI / path traversal | template render of input, ../ in file paths from input |
Domain references (load the ones that match your stack)
references/web-frontend.md— XSS, CSRF, CSP, client-side secrets, clickjackingreferences/api-backend.md— authz/IDOR, injection, SSRF, JWT, rate limiting, mass assignmentreferences/database.md— SQLi/NoSQLi, access control, data exposure, ORM pitfalls
Output: findings register
| ID | Severity | Title | Location | Source→sink | Exploitability | Fix pointer |
| F1 | High | SQLi in user search | api/users.py:42 | ?q= → raw SQL | Unauth, dumps DB | parameterize → security-hardening |
Severity = the threat-model risk score, adjusted for how exploitable the code actually is.
Hand-off
High-severity findings on a running app → active-pentest to validate.
All findings → security-hardening to fix.
Common mistakes
- Pattern-matching sinks without confirming input is actually attacker-controlled (false positive).
- Stopping at the first sink — one source often reaches several.
- Trusting client-side validation — it's bypassable; the server must validate.
- Ignoring authorization: most real-world breaches are access control, not exotic injection.
Does it work?
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Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.