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Sast

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H-mmer
sast

Source code vulnerability hunting (SAST). Decomposes analysis into specialized passes: map entry points, map dangerous ops, trace flows, find gaps, adversarial validation, exploit. Usage: /sast <repo_path> [--lang c|cpp|rust|java|python|go|php] [--min-score 4] [--max-files 30] [--skip-static] [--best-of N]

Overview

PublisherH-mmer
Repositorypentest-agents
Skill namesast
Stars
999
Forks
188
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by H-mmer on GitHub. Read the source before you install it.

Installation

Install the Sast 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/H-mmer/pentest-agents/tree/main/.claude/skills/sast
  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/H-mmer/pentest-agents.git /tmp/pentest-agents
mkdir -p .claude/skills
cp -r /tmp/pentest-agents/.claude/skills/sast .claude/skills/sast
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sast 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 Sast 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 Sast 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.

Source code hunt on: $ARGUMENTS

ALL agents dispatched by this command MUST use model: "inherit" in the Agent tool call, EXCEPT sast-flow-tracer and sast-gap-analyzer which MUST use model: "opus" (these require cross-file reasoning that benefits from maximum reasoning depth regardless of what the orchestrator inherits).

Read rules/hunting.md FIRST. Rules 0, 2, 9, 14 apply to SAST. Read skills/sast-methodology/SKILL.md for reference.

Why This Pipeline Exists

A single agent asked to "find vulnerabilities" will hallucinate plausible-looking bugs. This pipeline decomposes the task into focused steps:

  1. Reading code and listing entry points → comprehension task
  2. Listing dangerous operations → pattern matching task
  3. Connecting entry points to dangerous ops → cross-file reasoning (pinned opus)
  4. Finding validation gaps in those connections → focused analysis (pinned opus)
  5. Disproving each candidate → adversarial checking
  6. Building PoC for survivors → targeted coding

The synthesis happens through the PIPELINE, not inside one agent's head.

Phase 0: Setup

  1. Parse args: <repo_path>, --lang (auto-detect), --min-score (default 4), --max-files (default 30), --skip-static (skip CodeQL/Semgrep), --best-of N (run N independent hunters on top files, default 1)
  2. ls <repo_path>/
  3. Auto-detect language:
    bash
    find <repo_path> \( -name '*.c' -o -name '*.cpp' -o -name '*.h' -o -name '*.rs' -o -name '*.java' -o -name '*.py' -o -name '*.go' -o -name '*.php' -o -name '*.phtml' -o -name '*.inc' \) | head -20
  4. Check build: ls <repo_path>/{Makefile,CMakeLists.txt,Cargo.toml,pom.xml,go.mod,composer.json} 2>/dev/null
  5. Brain: uv run python3 $CLAUDE_PROJECT_DIR/tools/brain.py brief sast-<repo_name>
  6. Create output dirs: mkdir -p findings/sast poc/sast/exploits sast-work/

Phase 1: Build + Static Analysis

1a: Build with sanitizers (best-effort, C/C++/Rust/Go only)

bash
cd <repo_path>
export CC="gcc" CFLAGS="-fsanitize=address,undefined -g -O1 -fno-omit-frame-pointer"
export CXX="g++" CXXFLAGS="$CFLAGS"

If build fails → log and continue. Code review still works.

Skip 1a for PHP/Python/Java — no native sanitizers. For PHP, ensure php --version works and, if composer.json exists, run composer install --no-dev best-effort for autoload/deps.

1b: Static analysis (unless --skip-static)

Run available tools and collect warnings:

bash
# C/C++
cppcheck --enable=all --xml <repo_path> 2> sast-work/cppcheck.xml
# Universal
semgrep --config auto <repo_path> -o sast-work/semgrep.json --json
# PHP (run these when --lang php or .php files detected)
semgrep --config p/php --config p/security-audit <repo_path> -o sast-work/semgrep-php.json --json
psalm --taint-analysis --output-format=json <repo_path> > sast-work/psalm.json 2>/dev/null || true
phpstan analyse --level=max --error-format=json <repo_path> > sast-work/phpstan.json 2>/dev/null || true

Parse into sast-work/static-warnings.json. These feed into Phase 3d as additional candidates.

Phase 2: File Ranking

Dispatch sast-file-ranker agent (model: inherit):

  • Input: repo path, language, build info
  • Output: sast-rankings.json

Phase 3: Decomposed Analysis (per scored file)

For each file scoring >= --min-score, starting from highest:

3a: Entry Point Mapping

Dispatch sast-entry-mapper agent (model: inherit):

  • Output: sast-work/<file_hash>-entries.json

3b: Dangerous Operation Mapping

Dispatch sast-danger-mapper agent (model: inherit):

  • Output: sast-work/<file_hash>-dangers.json

3c: Data Flow Tracing

Dispatch sast-flow-tracer agent (model: opus):

  • Input: entries + dangers + source + headers
  • Output: sast-work/<file_hash>-flows.json

3d: Gap Analysis

Dispatch sast-gap-analyzer agent (model: opus):

  • Input: flows + static-warnings (if relevant)
  • Output: sast-work/<file_hash>-candidates.json

3e: Brain update

uv run python3 $CLAUDE_PROJECT_DIR/tools/brain.py record sast-<repo_name> analyzed "<file>" "entries: N, dangers: N, flows: N, candidates: N"

Phase 4: Adversarial Validation

For each candidate: Dispatch sast-devils-advocate agent (model: inherit):

  • Verdict: SURVIVES / KILLED (with reason)

Phase 5: PoC Confirmation

For each survivor: Dispatch sast-hunter agent (model: inherit) in focused mode:

  • Receives the specific candidate with full context
  • Writes PoC, runs with ASan (C/C++/Rust/Go) OR PHP runtime/HTTP request (PHP) OR interpreter (Python/Java)
  • Verdict: CONFIRMED / REJECTED

Best-of-N (if --best-of > 1)

For score-5 files, run N independent instances. Finding in 2+ runs = real. Finding in 1 run = flag for review.

Phase 6: Exploit Development (confirmed, severity >= medium)

Dispatch sast-exploit-builder agent (model: inherit):

  • Tier 1-5 exploitation ladder
  • Output: poc/sast/exploits/

Phase 7: Document

Record to brain, write to findings/sast/, print summary.

Output

SAST HUNT: <repo_name> (decomposed pipeline)
══════════════════════════════════════════════

Static warnings: N | Files ranked: N (huntable: N)
Entry points: N | Dangerous ops: N | Reachable flows: N | Candidates: N
Devil's advocate: N survived / N killed
ASan confirmed: N | Exploit tiers: ...

CONFIRMED:
1. [CRITICAL] <title> — <file>:<line>
   Flow: <entry> → <gap> → <dangerous op>

HALLUCINATIONS CAUGHT (saved by devil's advocate):
- <candidate> — killed: <reason>

Cost: $X.XX | Agents: N

Cost Awareness

4-6 agents per file, two using Opus. Budget ~$0.50-2.00/file. 30 files ≈ $15-60. Adjust --min-score and --max-files accordingly.

Top-Tier SAST Operator Addendum

Make source review adversarial and evidence-bound.

  1. Rank by exploit reachability, not scary APIs. Entry point plus attacker control plus missing guard plus dangerous sink beats isolated exec, eval, or deserializer references.
  2. For every candidate, require a concrete flow: source, transformations, authorization checks, validation gaps, sink, and trigger conditions.
  3. Read tests and recent security patches. Regression tests often show the intended invariant; patch diffs show the bug shape.
  4. Use static tools as lead generators only. A finding survives when the repo can be built or minimally exercised and the PoC hits the vulnerable path.
  5. Maintain a killed-candidate list with reasons: unreachable, sanitized, auth required, type impossible, dead code, framework guard, version mismatch.
  6. For memory-unsafe code, prefer sanitizer-confirmed crashes with minimized inputs. For web code, prefer request-level PoCs. For supply-chain or CI, prove the workflow trigger and trust boundary.
  7. Stop when marginal files are low-value. Raise --min-score rather than spending Opus on glue code.

Frequently asked questions

What does the Sast AI skill do?

Source code vulnerability hunting (SAST). Decomposes analysis into specialized passes: map entry points, map dangerous ops, trace flows, find gaps, adversarial validation, exploit. Usage: /sast <repo_path> [--lang c|cpp|rust|java|python|go|php] [--min-score 4] [--max-files 30] [--skip-static] [--best-of N]

Why use Sast on TypingMind?

Because you install it once and use it with any model. Sast 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 Sast in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/H-mmer/pentest-agents/tree/main/.claude/skills/sast. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Sast?

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 Sast?

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

Is the Sast AI skill free?

It is published on GitHub by H-mmer. Check the repository for licensing terms. 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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