ai-taint-scanner

ai-taint-scanner is a skill for Claude Code, Codex from marcosd4h/DeepExtractRuntime. It costs 77 tokens per session (3,258 once invoked), scanned A, original, MIT.

An AI-based scanner for tracking attacker-controlled data through Windows program files and identifying where it reaches dangerous operations. It enriches cross-DLL call paths with information about trust boundaries, parameters, sinks, and assembly structure, then independently checks findings.

In plain words
What is it for?
Use it to analyze RPC, COM, or exported entry points, trace input to file writes or privilege operations, and investigate cross-module data-flow security.
Why use it?
It helps security researchers follow untrusted input across multiple functions and modules instead of relying on simple pattern matching. This can expose risky paths that span trust boundaries.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: reads .claude/ paths; mentions subagents.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python .claude/skills/decompiled-code-extractor/scripts/find_module_db.py --list.

Good fit Use it to analyze RPC, COM, or exported entry points, trace input to file writes or privilege operations, and investigate cross-module data-flow security.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/marcosd4h/DeepExtractRuntime
agentmods
npx agentmods add skills/marcosd4h/deepextractruntime/ai-taint-scanner

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for ai-taint-scanner

README.md
[![agentmods](https://agentmods.dev/badge/skills/marcosd4h/deepextractruntime/ai-taint-scanner/github.svg)](https://agentmods.dev/skills/marcosd4h/deepextractruntime/ai-taint-scanner)
Your own site
<a href="https://agentmods.dev/skills/marcosd4h/deepextractruntime/ai-taint-scanner"><img src="https://agentmods.dev/badge/skills/marcosd4h/deepextractruntime/ai-taint-scanner/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for ai-taint-scanner

Your own site · 80×15
<a href="https://agentmods.dev/skills/marcosd4h/deepextractruntime/ai-taint-scanner"><img src="https://agentmods.dev/badge/skills/marcosd4h/deepextractruntime/ai-taint-scanner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 77 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,258 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00077 $0.03258
Opus 5 $0.00039 $0.01629
Sonnet 5 $0.00015 $0.00652
Haiku 4.5 $0.00008 $0.00326

Measured 9d ago against content hash f1ea8a53414f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

ai-taint-scanner scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/_common.py, scripts/build_threat_model.py, scripts/prepare_context.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/ai-taint-scanner/SKILL.md · 316 lines

How it starts

The opening of the file, as written. The whole thing — 316 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AI Taint Scanner

Purpose

Trace attacker-controlled data from entry points to dangerous sinks across module boundaries using LLM-driven analysis. The scanner builds a cross-module callgraph from attacker-reachable entry points, classifies every node (MUST_READ / KNOWN_API / TELEMETRY / LIBRARY), enriches each node with taint-specific metadata (sink density, parameter types, trust boundaries, assembly CFG summary), and delivers the enriched context to the AI agent in depth-level batches. The agent traces taint forward through each batch, returns requests for deeper functions, and reads deeper code itself via Shell -- iterating until max depth or taint termination.

This is NOT a pattern scanner. All taint propagation decisions are made by the LLM agent using the enriched callgraph context. A separate skeptic agent independently verifies each finding against assembly ground truth.

When to Use

  • Trace attacker-controlled parameters from RPC/COM/export entry points to dangerous sinks (file writes, memory copies, privilege operations)
  • Analyze cross-module data flow security across DLL boundaries
  • Find where untrusted input reaches trust boundary crossings
  • Identify parameter propagation chains from network-facing handlers to privileged operations
  • AI-driven taint analysis on decompiled Windows binaries when deeper cross-module context is needed

When NOT to Use

  • Memory corruption scanning (buffer overflows, UAF) -- use ai-memory-corruption-scanner
  • Logic vulnerabilities (auth bypass, state errors) -- use ai-logic-scanner
  • Code lifting or rewriting -- use code-lifter
  • General function explanation -- use re-analyst or /explain

Data Sources

SQLite Databases (primary)

Individual analysis DBs in extracted_dbs/ provide the raw data:

  • functions.decompiled_code -- Hex-Rays decompiled C (read on demand)
  • functions.assembly_code -- x64 assembly ground truth (read on demand)
  • functions.simple_outbound_xrefs -- Callgraph edges
  • functions.function_signature -- Parameter types and names
  • functions.global_var_accesses -- Global variable reads/writes
  • functions.loop_analysis -- Loop structure and complexity
  • file_info.exports -- Exported functions
  • file_info.imports -- Imported APIs
  • file_info.entry_point -- PE entry points

Read the full file on GitHub · 316 lines

Files

What ships with it

6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 316 lines · 77 tokens per session scan A f1ea8a53414f

Subscribe to this mod's changes

ai-taint-scanner is a skill published in the GitHub repository marcosd4h/DeepExtractRuntime (19 stars, last pushed 4mo ago), licensed MIT. It adds 77 tokens to every session and 3,258 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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