Agent skill · Security

zeroize-audit

Detects missing zeroization of sensitive data in source code and identifies zeroization removed by compiler optimizations, with assembly-level analysis, and control-flow verification. Use for auditing C/C++/Rust code handling secrets, keys, passwords, or other sensitive data.

trailofbitsgithub.com/trailofbitsGitHub ↗
claude-codecan modify filesships scriptsCC-BY-SA-4.0
Install
npx skills add trailofbits/skills --skill zeroize-audit --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 50
SKILL.md size: 20 KB
Bundled scripts: yes
Allowed tools: ReadGrepGlobBashWriteTaskAskUserQuestionmcp__serena__activate_projectmcp__serena__find_symbolmcp__serena__find_referencing_symbolsmcp__serena__get_symbols_overview
Path: plugins/zeroize-audit/skills/zeroize-audit/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 6,426
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# zeroize-audit — Claude Skill ## When to Use - Auditing cryptographic implementations (keys, seeds, nonces, secrets) - Reviewing authentication systems (passwords, tokens, session data) - Analyzing code that handles PII or sensitive credentials - Verifying secure cleanup in security-critical codebases - Investigating memory safety of sensitive data handling ## When NOT to Use - General code review without security focus - Performance optimization (unless related to secure wiping) - Refactoring tasks not related to sensitive data - Code without identifiable secrets or sensitive values --- ## Purpose Detect missing zeroization of sensitive data in source code and identify zeroization that is removed or weakened by compiler optimizations (e.g., dead-store elimination), with mandatory LLVM IR/asm evidence. Capabilities include: - Assembly-level analysis for register spills and stack retention - Data-flow tracking for secret copies - Heap allocator security warnings - Semantic IR analysis for loop unrolling and SSA form - Control-flow graph analysis for path coverage verification - Runtime validation test generation ## Scope - Read-only against the target codebase (does not modify audi

What's inside
Steps it walks through
  1. When to Use
  2. When NOT to Use
  3. Purpose
  4. Scope
  5. Inputs
  6. Prerequisites
  7. Approved Wipe APIs
  8. Finding Capabilities
  9. Agent Architecture
  10. Execution flow
  11. Cross-Reference Convention
  12. Detection Strategy
  13. Output Format
  14. Markdown Report Structure
Ships with 24 files
  • agents/openai.yaml
  • assets/trail-of-bits-mark.svg
  • configs/c.yaml
  • configs/default.yaml
  • configs/rust.yaml
  • prompts/report_template.md
  • prompts/system.md
  • prompts/task.md
  • references/compile-commands.md
  • references/detection-strategy.md
  • references/ir-analysis.md
  • references/mcp-analysis.md
  • references/poc-generation.md
  • references/rust-zeroization-patterns.md
  • schemas/input.json
  • schemas/output.json
  • tools/analyze_asm.sh
  • tools/analyze_cfg.py
  • tools/analyze_heap.sh
  • tools/analyze_ir_semantic.py
  • tools/diff_ir.sh
  • tools/diff_rust_mir.sh
  • tools/emit_asm.sh
  • tools/emit_ir.sh
first 24 of 50
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About this skill
What does the zeroize-audit skill do?

Detects missing zeroization of sensitive data in source code and identifies zeroization removed by compiler optimizations, with assembly-level analysis, and control-flow verification. Use for auditing C/C++/Rust code handling secrets, keys, passwords, or other sensitive data.

How do I install it?

Run `npx skills add trailofbits/skills --skill zeroize-audit --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From trailofbits/skills, a repository with 6,426 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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