dwarf-expert
The dwarf-expert skill provides technical knowledge for analyzing and working with DWARF debug information (versions 3 through 5) found in compiled binaries. Use this skill when parsing DWARF files, answering questions about the DWARF standard, writing code that interacts with DWARF data, or using tools like dwarfdump and llvm-dwarfdump to extract and verify debug information from binaries.
git clone --depth 1 https://github.com/trailofbits/skills /tmp/dwarf-expert && cp -r /tmp/dwarf-expert/plugins/dwarf-expert/skills/dwarf-expert ~/.claude/skills/dwarf-expertSKILL.md
# DWARF Expert Expertise for DWARF debug info: parsing and searching it, verifying its integrity, answering questions about the standard, and writing code that consumes it. Out of scope: runtime debugging (use gdb/lldb), reverse engineering beyond the DWARF sections (use Ghidra/IDA), and compiler-specific DWARF generation bugs. # Authoritative Sources When precision matters, look standard details up instead of answering from memory: 1. **dwarfstd.org** — the official specification. Web-search specific sections, e.g. "DWARF5 DW_TAG_subprogram attributes site:dwarfstd.org". 2. **LLVM** — `llvm/lib/DebugInfo/DWARF/` is a reliable reference implementation: `DWARFDie.cpp` (DIE and attribute access), `DWARFUnit.cpp` (compilation units), `DWARFDebugLine.cpp` (line tables), `DWARFVerifier.cpp` (validation). 3. **libdwarf** — the reference C implementation at github.com/davea42/libdwarf-code. # Parsing and Searching with dwarfdump Prefer `dwarfdump` over `readelf` for DWARF-specific work. Two implementations exist — libdwarf's `dwarfdump` and LLVM's `llvm-dwarfdump` — with different options, and a bare `dwarfdump` command may be either: check `dwarfdump --version` first. The options below are LLVM's. On macOS, linked Mach-O executables do not carry DWARF: it stays in the `.o` files until `dsymutil` collects it into a `.dSYM` bundle. Point `dwarfdump` at the dSYM (or the object files), not the executable. `pyelftools` is ELF-only — for Mach-O scripted work, stay with the LLVM tools. - `--all`: dump every DWARF section; `--debug-info`, `--debug-line`, etc. dump one - `--show-children [--recurse-depth=<n>]`: include child DIEs when printing selected entries — parameters, locals, and struct members are children of function and type DIEs - `--show-parents [--parent-recurse-depth=<n>]`: include parent DIEs - `--show-form`: print attribute form types, for when encoding details matter - `--find=<name>`: exact-name lookup via the accelerator tables — fast but not exhaustive; fall back to `--name` when it misses - `--name=<pattern> [--ignore-case] [--regex]`: exhaustive DIE-name search - `--lookup=<address>`: find the DIE covering an address - `--verbose`: print low-level encoding detail ## Searching DIEs Escalate through these strategies as the query grows more complex: 1. **Name or address match**: `--find`, then `--name`; `--lookup` for addresses. 2. **Attribute or type queries** (e.g. all parameters of type `float *`): dump and filter. `grep -B` pulls in the header line carrying each DIE's offset: `llvm-dwarfdump file | grep -B 5 "float \*" | grep DW_TAG_formal_parameter`, then print each DIE at its offset with `--debug-info=<offset> --show-children` (`--lookup` takes a program address, not a DIE offset). 3. **Multi-attribute or structural queries**: when grep pipelines turn brittle, write a Python script using `pyelftools` instead. # Verifying DWARF Integrity - `llvm-dwarfdump --verify <binary>`: structural checks (unit chains, DIE relationships, address ranges). `--error-display=<quiet|summary|details|full>` controls detail; `--verify-json=<path>` writes a machine-readable error summary; `--quiet` for exit-code-only checks. - `llvm-dwarfdump --statistics <binary>`: debug-info quality metrics as JSON — compare across compiler versions or optimization levels to catch regressions. Verify after producing DWARF (compilers, binary rewriters), when a debugger misbehaves on a binary, and when developing DWARF tooling against known-good files. When a current-generation compiler emitted an old DWARF version, the build explicitly passed `-gdwarf-N` — modern gcc and clang default to v4/v5, so check the build system rather than assuming a toolchain default. GCC embeds its flags in `DW_AT_producer`, so the pin is often readable right there; clang's producer string carries no flags. Old versions remain common in the wild and read the same way apart from surface forms: in v2 output, member offsets appear as location expressions (`DW_OP_plus_uconst`) and linkage names as `DW_AT_MIPS_linkage_name`. # readelf For general ELF structure, or when `dwarfdump` is unavailable: - `--debug-dump=<section>`: dump a DWARF section (`info`, `line`, ...) - `--dwarf-depth=<n>` / `--dwarf-start=<n>`: limit DIE depth / start offset # Writing Code That Parses DWARF Prefer an existing library over parsing by hand: | Library | Language | Notes | |---------|----------|-------| | `libdwarf` | C/C++ | github.com/davea42/libdwarf-code — low-level; used to implement `dwarfdump` | | `pyelftools` | Python | github.com/eliben/pyelftools — also parses ELF in general | | `gimli` | Rust | github.com/gimli-rs/gimli — pair with `object` to load container files | | `debug/dwarf` | Go | standard library | | `LibObjectFile` | .NET | github.com/xoofx/LibObjectFile — also handles ELF/PE object files | Default to Python with `pyelftools` for one-off scripts unless the task dictates otherwise. DWARF-specific pitfalls to handle — and to check for when reviewing DWARF code: - Attributes are optional: a DIE may omit `DW_AT_name`, `DW_AT_type`, ranges, etc. - Attribute indirection: a DIE's attributes may live on the DIE referenced by its `DW_AT_abstract_origin` (inlined instances) or `DW_AT_specification` (out-of-line definitions) — resolve the chain before concluding data is absent. - Type chains: qualifiers and modifiers (`DW_TAG_const_type`, `DW_TAG_pointer_type`, ...) wrap the underlying type; walk `DW_AT_type` links to reach the base type.
Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches AI agents running in CI/CD pipelines, including env var intermediary patterns, direct expression injection, dangerous sandbox configurations, and wildcard user allowlists. Use when reviewing workflow files that invoke AI coding agents, auditing CI/CD pipeline security for prompt injection risks, or evaluating agentic action configurations.
Clarify requirements before implementing. Use when serious doubts arise.
Understand a codebase before looking for bugs in it - what each function assumes, what it guarantees, and what it depends on elsewhere. Use when starting an audit, threat model, or architecture review on unfamiliar code, and before any vulnerability-hunting pass.
Scans Algorand smart contracts for 11 common vulnerabilities including rekeying attacks, unchecked transaction fees, missing field validations, and access control issues. Use when auditing Algorand projects (TEAL/PyTeal).
Prepares codebases for security review using Trail of Bits' checklist. Helps set review goals, runs static analysis tools, increases test coverage, removes dead code, ensures accessibility, and generates documentation (flowcharts, user stories, inline comments). Use when preparing your own codebase to be audited by someone else, getting a repository review-ready before an external security review, deciding what to fix before auditors start, or asking what assessors need from a project. For understanding unfamiliar code you are about to audit, use audit-context-building instead.
Scans Cairo/StarkNet smart contracts for 6 critical vulnerabilities including felt252 arithmetic overflow, L1-L2 messaging issues, address conversion problems, and signature replay. Use when auditing StarkNet projects.
Systematic code maturity assessment using Trail of Bits' 9-category framework. Analyzes codebase for arithmetic safety, auditing practices, access controls, complexity, decentralization, documentation, MEV risks, low-level code, and testing, then produces a scorecard with evidence-based ratings and a priority-ordered roadmap. Use when assessing or scoring the maturity of a smart contract or blockchain codebase, producing a maturity scorecard or evaluation, or judging how mature, well-tested, or well-documented such a project is against a rubric.
Scans Cosmos SDK blockchain modules and CosmWasm contracts for consensus-critical vulnerabilities — chain halts, fund loss, state divergence. 25 core + 16 IBC + 10 EVM + 3 CosmWasm patterns. Use when auditing custom x/ modules, reviewing IBC integrations, or assessing pre-launch chain security. Updated for SDK v0.53.x.