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Vulnerability Analysis

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hypnguyen1209
vulnerability-analysis

Use when auditing source code for vulnerabilities — drive CodeQL/Semgrep/Joern to taint untrusted data source-to-sink across injection, memory safety, deserialization/prototype-pollution, secrets/crypto/authz/race, and supply-chain risks

Overview

Publisherhypnguyen1209
Repositoryoffensive-claude
Skill namevulnerability-analysis
Stars
396
Forks
71
Bundled files
21
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 21 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by hypnguyen1209 on GitHub. Read the source before you install it.

Installation

Install the Vulnerability Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/hypnguyen1209/offensive-claude/tree/main/skills/vulnerability-analysis
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/hypnguyen1209/offensive-claude.git /tmp/offensive-claude
mkdir -p .claude/skills
cp -r /tmp/offensive-claude/skills/vulnerability-analysis .claude/skills/vulnerability-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Vulnerability Analysis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Vulnerability Analysis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Vulnerability Analysis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Vulnerability Analysis

Every vulnerability you miss is one an attacker finds first. Systematic source auditing traces untrusted data from source to sink, evaluates every sanitizer for bypass, and questions each trust-boundary assumption. This skill is the router; depth lives in references/, and every technique cluster is backed by a runnable tool in scripts/.

When to Activate

  • Auditing any codebase (white/grey box) for security vulnerabilities
  • Writing/driving CodeQL queries, Semgrep taint rules, or Joern CPGQL flows
  • Tracing injection, deserialization, prototype-pollution, or memory-safety paths
  • Reviewing auth/authorization (IDOR/BOLA), cryptography, or concurrency (TOCTOU)
  • Triaging dependency CVEs and hunting malicious/compromised packages (SCA)
  • Variant hunting — generalizing one finding into a codebase-wide pattern

Technique Map

TechniqueATT&CKCWEReferenceScript
Taint analysis (source/sink/sanitizer modeling)T1190CWE-20references/taint-engines-static-analysis.mdscripts/taint_trace.py
CodeQL/Semgrep/Joern engine orchestration + mergeT1190CWE-20references/taint-engines-static-analysis.mdscripts/sast_runner.py, scripts/joern_taint.sc
SQL injection (raw/ORM/identifier/2nd-order/NoSQL)T1190CWE-89references/injection-source-patterns.mdscripts/taint_trace.py
OS command / argument injectionT1059CWE-78 / CWE-88references/injection-source-patterns.mdscripts/taint_trace.py
Server-side template injectionT1190CWE-1336references/injection-source-patterns.mdscripts/taint_trace.py
Path traversalT1083CWE-22references/injection-source-patterns.mdscripts/taint_trace.py
SSRF (tainted server-side URL)T1190CWE-918references/injection-source-patterns.mdscripts/taint_trace.py
Integer overflow -> heap/stack overflowT1203CWE-190 / CWE-787references/memory-safety-c-cpp.mdscripts/joern_taint.sc
Use-after-free / double-freeT1203CWE-416 / CWE-415references/memory-safety-c-cpp.mdscripts/joern_taint.sc
Unbounded copy / NULL derefT1203CWE-120 / CWE-476references/memory-safety-c-cpp.mdscripts/joern_taint.sc
Insecure deserialization + gadget preconditionsT1059CWE-502references/deserialization-prototype-pollution.mdscripts/deser_gadget_scan.py
Prototype pollution -> gadget -> RCET1059.007CWE-1321references/deserialization-prototype-pollution.mdscripts/deser_gadget_scan.py
Hardcoded secrets / high-entropy literalsT1552.001CWE-798 / CWE-321references/secrets-crypto-authz-concurrency.mdscripts/secret_crypto_audit.py
Weak / misused cryptographyT1600CWE-327 / CWE-328 / CWE-330 / CWE-347references/secrets-crypto-authz-concurrency.mdscripts/secret_crypto_audit.py
Broken authorization / IDOR / BOLAT1190CWE-639 / CWE-862references/secrets-crypto-authz-concurrency.mdscripts/secret_crypto_audit.py
TOCTOU / race conditionT1190CWE-367 / CWE-362references/secrets-crypto-authz-concurrency.mdscripts/secret_crypto_audit.py
Known-CVE dependency (SCA)T1195.001CWE-1395 / CWE-1104references/supply-chain-dependency-audit.mdscripts/dep_audit.py
Malicious package / install worm / typosquatT1195.002CWE-506 / CWE-829 / CWE-1357references/supply-chain-dependency-audit.mdscripts/dep_audit.py
Variant hunting — one finding -> all siblings, root-cause clusteredT1190CWE-20references/variant-hunting.mdscripts/variant_hunt.py
Path feasibility — branch guards -> tri-state SAT/UNSAT (Z3 optional)T1190CWE-20references/taint-engines-static-analysis.mdscripts/path_conditions.py
Evidence grounding + FP gate (structured proof, EVD citations)T1190CWE-20references/finding-validation-runtime.mdscripts/validate_findings.py, scripts/evidence_kit.py
Runtime reachability confirmation (Frida sink-executed)T1190CWE-20references/dynamic-instrumentation.mdscripts/merge_runtime_evidence.py

Quick Start

bash
# 0. Intake from recon-osint: languages, frameworks, trust boundaries, entry points.

# 1. Fast triage — rank files by unsanitized source->sink flows (heuristic, multi-lang)
python3 scripts/taint_trace.py trace ./src --lang py,js,php,java --json triage.json
python3 scripts/taint_trace.py config --lang py > seed.semgrep.yml   # seed a real rule

# 2. Deep interprocedural — drive the real engines, then merge into one ranked list
python3 scripts/sast_runner.py semgrep --src ./src --config p/owasp-top-ten --pro --out sg.sarif
python3 scripts/sast_runner.py codeql  --src ./src --lang python \
    --suite codeql/python-queries:codeql-suites/python-security-extended.qls --out cq.sarif
python3 scripts/sast_runner.py joern   --src ./src --script scripts/joern_taint.sc --out joern.json
python3 scripts/sast_runner.py merge   sg.sarif cq.sarif joern.json --out merged.json --top 50

# 3. Class-specific deep passes
python3 scripts/deser_gadget_scan.py  all ./src --json deser.json     # deser sinks + gadget libs
python3 scripts/secret_crypto_audit.py all ./src --json sca.json      # secrets+crypto+authz+TOCTOU
python3 scripts/dep_audit.py          all ./repo --json dep.json      # CVE SCA + worm/typosquat
trufflehog filesystem ./src --only-verified                          # confirm LIVE secrets

# 4. Exploitability gate (per finding): reachable? controllable? real impact? sanitizer real?
#    -> write confirmed issues to templates/exploit/findings/ with
#       severity, CWE, CVSS, taint path, PoC, evidence, ATT&CK ID, remediation.
python3 scripts/evidence_kit.py verify --store evidence.json            # re-hash every artifact
python3 scripts/validate_findings.py --findings findings.json \
    --evidence ./evidence --evidence-store evidence.json --strict       # tier + EVD-citation gate

# 5. After a CONFIRMED finding: hunt every sibling, then prune false-positive paths
python3 scripts/variant_hunt.py ./src --seed-finding findings.json --lang py,js --json variants.json
python3 scripts/taint_trace.py trace ./src --lang py --json trace.json   # trace carries branch guards
python3 scripts/path_conditions.py --trace trace.json --json feasibility.json  # Z3 prune (tri-state)
python3 scripts/merge_runtime_evidence.py --events events.jsonl --findings findings.json --out merged.json

OPSEC & Detection (summary)

TechniqueTelemetry / IOCDetection (Sigma/EDR)OPSEC note
SAST engine runscodeql database create, semgrep --sarif, joern-parse process trees; large codeql-db//cpg.binCI process-creation Sigma (informational)offline & silent; CodeQL --command runs target build -> sandbox hostile repos; purge DBs/SARIF on teardown
Injection (SQLi/cmdi/SSTI/SSRF)SQL keywords/SLEEP, web svc spawning sh/curl, template engine errors, OOB DNSwebserver regex + EDR child-shell Sigmasource review is noiseless; validate with time-based/DNS-OOB, never --dump
Memory safety (C/C++)SIGSEGV/SIGABRT, glibc corrupted/double free, ASan reportsauditd ANOM_ABEND Sigma; EDR RWX/W^X alertsCPG review silent; fuzz a local copy in a container, purge cores (may hold secrets)
Deserialization / proto-pollutionJava rO0AB/.NET AAEAAAD///// in bodies; JVM->LDAP/RMI; POST / w/ Next-Action (CVE-2025-55182)egress Sigma to 389/636/1099/1389; body-marker rulesprove gadget chain exists; id/DNS callback not reverse shell; proto-pollution is global -> revert
Secrets / crypto / authz / TOCTOUAKIA*/ghp_* patterns; alg:none; sequential-id 200s; symlink-race auditdsecret-scanning push protection; symlink/PATH auditdsecret verification makes a logged provider API call -> only in scope; read one record for IDOR/TOCTOU evidence
Supply chain (SCA / worm)install hook spawning shell/net; bundle.js/setup_bun.js; new public Shai-Hulud repoinstall-script process-creation Sigma; agentless SBOM lookupnever npm install a suspect tree; --ignore-scripts in a disposable container; treat a compromised dep as full host compromise

Deep Dives

  • references/taint-engines-static-analysis.md — taint theory + propagation rules; Semgrep (taint mode, Pro interfile, Opengrep), CodeQL modular dataflow API, Joern CPG/reachableBy; engine orchestration + multi-tool merge; CPG+LLM (2025).
  • references/injection-source-patterns.md — source-code shapes of SQLi (incl. identifier/ORDER BY/2nd-order/NoSQL), OS command & argument injection, SSTI, path traversal, SSRF; per-language safe/vuln pairs; Joern/Semgrep confirmation.
  • references/memory-safety-c-cpp.md — integer overflow->overflow, UAF/double-free, unbounded copy, NULL deref; the 2025 libxml2 CVE cluster; ASan/UBSan + fuzzing confirmation; unsafe Rust surface.
  • references/deserialization-prototype-pollution.md — CWE-502 sinks + gadget preconditions (ysoserial/phpggc/ysoserial.net), JS prototype pollution->RCE; React2Shell CVE-2025-55182, Tomcat CVE-2025-24813, lodash CVE-2025-13465, Silent Spring.
  • references/secrets-crypto-authz-concurrency.md — hardcoded secrets (gitleaks/ trufflehog/Kingfisher), weak crypto, IDOR/BOLA, TOCTOU/race; LLM-augmented secret detection (2025).
  • references/supply-chain-dependency-audit.md — OSV/SCA, malicious-package & install worm heuristics, typosquats; Shai-Hulud 1.0/2.0/Mini (CVE-2026-45321), the SLSA provenance-bypass lesson.
  • references/variant-hunting.md — HUNT protocol: one confirmed finding -> tree-wide sibling sweep, same-function taint qualification, root-cause clustering (copy-paste/shared-util/ framework-misuse) to decide one-fix-or-many; backed by variant_hunt.py. Pairs with evidence_kit.py (re-verifiable EVD evidence), path_conditions.py (Z3 path-prune, tri-state), and merge_runtime_evidence.py (Frida runtime sink confirmation).

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Vulnerability Analysis AI skill do?

Use when auditing source code for vulnerabilities — drive CodeQL/Semgrep/Joern to taint untrusted data source-to-sink across injection, memory safety, deserialization/prototype-pollution, secrets/crypto/authz/race, and supply-chain risks

Why use Vulnerability Analysis on TypingMind?

Because you install it once and use it with any model. Vulnerability Analysis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Vulnerability Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hypnguyen1209/offensive-claude/tree/main/skills/vulnerability-analysis. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Vulnerability Analysis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Vulnerability Analysis?

As many as you like. As long as a model supports skills, you can use Vulnerability Analysis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Vulnerability Analysis AI skill free?

Yes. It is published on GitHub by hypnguyen1209 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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