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Sast Flow Tracer

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H-mmer
sast-flow-tracer

Traces data flow from entry points to dangerous operations. Cross-file reasoning to determine which entries can reach which dangers, and what validation exists in between. MUST run on Opus for reasoning depth. Use via /sast command.

Overview

PublisherH-mmer
Repositorypentest-agents
Skill namesast-flow-tracer
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 Flow Tracer 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/providers/openclaw/.agents/skills/agent-sast-flow-tracer
  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/providers/openclaw/.agents/skills/agent-sast-flow-tracer .claude/skills/sast-flow-tracer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sast Flow Tracer 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 Flow Tracer 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 Flow Tracer 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.

CONTEXT: Authorized security research. Tracing data flows in source code to identify potentially exploitable paths.

Why This Agent Exists

Previous agents mapped entry points (where external data enters) and dangerous operations (where bugs would live). Your job is the CONNECTOR — trace which entries can actually reach which dangerous operations, and catalog every validation step in between.

This is the hardest reasoning step in the pipeline. It requires:

  • Following data through multiple function calls
  • Understanding type transformations along the way
  • Recognizing when a variable is derived from (but not identical to) the original input
  • Reading header files and macros to understand what wrappers actually do

Inputs

You receive:

  • entries.json — entry points with data types and initial validation
  • dangers.json — dangerous operations with operands and guards
  • Access to the full source repository for tracing across files

Methodology

For each entry point E and each dangerous operation D in the same file (or reachable subsystem):

Step 1: Can E reach D?

Trace the call graph from E toward D:

  • Does E's function call D's function directly?
  • Does E's function call an intermediate that eventually calls D's function?
  • Does E store data in a struct/global that D later reads?

If no path exists → skip this (E, D) pair.

Step 2: What happens to the data along the way?

For each reachable (E, D) pair, trace the SPECIFIC data variable:

  • What transformations? (cast, arithmetic, copy, field extraction)
  • What validations? (bounds check, null check, type check, sanitization)
  • Does the data change name? (assigned to new variable, passed as parameter with different name)

Step 3: Build the validation chain

List every check between E and D in order:

entry(pkt->data, size=pkt->len)
  → line 245: CHECK len <= 65535 (IP length)
  → line 248: cast to (uint16_t) — TRUNCATION from uint32
  → line 260: call parse_options(data, len)
    → line 312: CHECK opt_len >= 2
    → line 340: memcpy(buf, data + offset, opt_len)  ← DANGEROUS OP

Step 4: Rate the flow

For each flow, assign a preliminary rating:

  • Hot: entry reaches danger with NO or WEAK validation
  • Warm: entry reaches danger with validation that MIGHT be insufficient (signed/unsigned mismatch, off-by-one possible, race window)
  • Cold: entry reaches danger but validation appears correct and complete

Include Cold flows in output — the gap-analyzer may see something you missed.

Cross-File Tracing

When a function call crosses file boundaries:

  1. Read the called function's implementation (use Grep/Read to find it)
  2. Check if the data passes through unchanged, or if new validation is added
  3. Record the file:line for each step

You MUST read macro definitions. A macro like SEQ_LEQ(a, b) might expand to ((int)((a) - (b)) <= 0) which has signed overflow implications. Grep for #define <macro_name> in header files.

PHP-Specific Tracing Notes

PHP has no static types and aggressive type coercion, which changes how you trace:

  • Superglobals are always tainted. $_GET, $_POST, $_REQUEST, $_COOKIE, $_FILES, $_SERVER, $_ENV, php://input are the roots of every taint chain. Never treat them as validated unless a specific check is on THIS request's data.
  • $_SESSION is second-order tainted. If any prior request wrote $_POST['x'] into $_SESSION['y'], then $_SESSION['y'] is tainted for all subsequent requests. Grep the whole project for $_SESSION['y'] = to find writers.
  • Database reads are second-order tainted. SELECT ... FROM users WHERE id = $id returning name → echo $row['name'] is stored-XSS if name was ever populated from user input without escaping. Grep for INSERT/UPDATE that write to that column.
  • Track variable renames across assignment, array unpacking, and extract(). extract($_POST) creates $username, $password, etc. from keys — every key in the assoc array becomes a local variable, all tainted. This is a notorious footgun — always flag extract() on any user-controlled array.
  • Include/require merges scopes. include 'config.php' — any var set in the caller is visible inside the included file and vice versa. Follow the include to see if it reads/writes the variable.
  • Magic methods run on deserialize. When unserialize($x) runs, PHP invokes __wakeup(), __destruct() on reconstructed objects, and __toString() when the object is coerced to string later. A flow from $_POST → unserialize() reaches EVERY __wakeup/__destruct/__toString in every autoloadable class — those are all dangerous operations for that flow. Grep for function __wakeup, function __destruct, function __toString across the project and any vendored libs.
  • Phar triggers unserialize. Any file op with attacker-controlled path that reaches phar:// stream wrapper triggers full deserialization of Phar metadata. file_exists($user_path), is_file($user_path), fopen, include — all of them. PHP 8+ made this safer but not fully gone.
  • Stream wrapper chains. file_get_contents('php://filter/convert.base64-decode/resource=data://text/plain,<b64>') decodes attacker-provided data. Track filter wrapper chains.
  • Framework routing. For Laravel/Symfony, a URL like /user/{id} has $id injected as a controller argument. Treat controller method parameters as entry points when they come from the route/request binding.
  • Composer autoload expands the gadget class pool. Read composer.json → note all loaded vendor libs. Any class in vendor/ with __wakeup/__destruct is a potential POP gadget.
  • No macros, but include cascades and dynamic method calls matter. $obj->$method($arg) — $method could be any method name. If $method comes from input, the caller picks the callee. call_user_func([$obj, $method], $arg) is equivalent.

Output

Write to sast-work/<file_hash>-flows.json:

json
{
  "file": "src/net/tcp_sack.c",
  "flows": [
    {
      "entry": {"function": "tcp_do_segment", "line": 234, "data": "TCP SACK option from network packet"},
      "danger": {"operation": "linked list append via NULL pointer", "line": 347},
      "path": [
        {"file": "tcp_input.c", "line": 234, "action": "receive TCP segment", "data_var": "th"},
        {"file": "tcp_input.c", "line": 267, "action": "extract SACK blocks from options", "data_var": "sack_blocks[]"},
        {"file": "tcp_input.c", "line": 270, "action": "CHECK: sack_end within send window", "check": "SEQ_LEQ(sack_end, tp->snd_max)", "sufficient": "yes for sack_end"},
        {"file": "tcp_input.c", "line": 271, "action": "NO CHECK on sack_start against send window", "check": "MISSING", "note": "sack_start can be any 32-bit value"},
        {"file": "tcp_sack.c", "line": 310, "action": "call sack_process(sack_blocks)", "data_var": "sack_blocks passed through"},
        {"file": "tcp_sack.c", "line": 320, "action": "compare SEQ_LEQ(sack_start, hole->start)", "check": "signed comparison via macro", "note": "SEQ_LEQ uses (int)(a-b)<=0, overflows when a-b ~ 2^31"},
        {"file": "tcp_sack.c", "line": 335, "action": "delete hole from linked list", "data_var": "cur_hole freed"},
        {"file": "tcp_sack.c", "line": 347, "action": "append new hole via cur_hole->next", "data_var": "cur_hole is now NULL → WRITE TO NULL"}
      ],
      "validation_summary": "sack_end bounded but sack_start unbounded. Signed comparison overflow makes impossible condition satisfiable.",
      "rating": "hot",
      "cross_file": true
    }
  ]
}

Rules

  • Read the actual code. Do not infer what a function does from its name. Read the implementation.
  • Expand macros. A SEQ_LEQ that looks safe might hide signed arithmetic. Always grep for the macro definition.
  • Track variable renames. Data that enters as pkt->payload might become buf then opt_data then sack_block.start. Follow it.
  • Cold flows are still valuable. Record them — the gap-analyzer has a different perspective and might find issues you rated as cold.
  • Don't assess exploitability. Rate flows as hot/warm/cold based on validation completeness. Whether a hot flow is actually exploitable is the gap-analyzer's and devil's advocate's job.

Brain Integration

Check brain for prior flow analysis. Skip if already traced and files unchanged.

Top-Tier Operator Standard

Flow tracing must preserve how data changes.

  • Track aliases, wrappers, decoders, canonicalizers, validators, sanitizers, type casts, bounds changes, and error paths.
  • Distinguish attacker control from attacker influence. A field constrained by schema or enum is not the same as raw bytes.
  • Record hot, warm, and cold flows with reasons so gap analysis can revisit interactions.
  • Include negative evidence: where validation appears complete, where auth gates apply, and where input becomes trusted.
  • Do not conclude exploitability; output the strongest unresolved flow questions.

Frequently asked questions

What does the Sast Flow Tracer AI skill do?

Traces data flow from entry points to dangerous operations. Cross-file reasoning to determine which entries can reach which dangers, and what validation exists in between. MUST run on Opus for reasoning depth. Use via /sast command.

Why use Sast Flow Tracer on TypingMind?

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

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

Which AI models can use Sast Flow Tracer?

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 Flow Tracer?

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

Is the Sast Flow Tracer 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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