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skillctl: argument injection, path traversal in --dest, FIFO/device DoS, hardlink exfiltration, and commit-trailer forgery

High
umanio-agency published GHSA-74p7-6h78-gw8p May 21, 2026

Package

cargo skillctl (Rust)

Affected versions

< 0.1.3

Patched versions

0.1.3

Description

Impact

Following the path-safety patches in GHSA-wx3m-whqv-xv47 (v0.1.2), a comprehensive multi-angle audit surfaced five further vulnerabilities, now patched in v0.1.3:

  1. source_sha argument injection in git ls-tree (CRITICAL). InstalledSkill.source_sha deserialized from .skills.toml (committed, PR-mergeable) flowed unvalidated into git ls-tree -r -z <refspec> -- <path>. Because the refspec sits before --, an attacker who slipped a malicious .skills.toml into a PR could set source_sha = "--name-only" / --abbrev=0 / --output=… and corrupt the diff classifier (which drives pull / push destructive decisions), or forge a divergence state to trick push --on-divergence overwrite into clobbering the wrong content.
  2. FIFO / device / socket denial-of-service in copy_dir_all (CRITICAL). The file-type branch only checked is_dir() / is_symlink(); a FIFO inside a skill folder fell through to fs::copy, which blocks indefinitely waiting for a writer. A character device like /dev/zero would read until OOM. Reachable on skillctl add against any adversarial library.
  3. add --dest arbitrary-directory wipe in agent mode (HIGH). --dest accepted absolute paths and .. traversal without validation, so a single invocation skillctl add --dest /Users/victim/.ssh --on-conflict overwrite --skill <maliciously-named> would remove_dir_all arbitrary directories — no .skills.toml round-trip required. Reachable in any non-interactive / agent-driven workflow where flag values may be attacker-supplied.
  4. Commit-message trailer forgery via skill names (HIGH). Skill names were spliced verbatim into git commit -m "update skill: <name>" and into the commit.message field of --json output. A skill named foo\nCo-Authored-By: evil@x produced a forged commit trailer that downstream tooling (Linear, GitHub commit-bot, release-notes scrapers) treats as real authorship metadata.
  5. Hardlink exfiltration via the round-trip (HIGH). fs::symlink_metadata reports a regular file for hardlinks (shared inode), and fs::copy reads the target content. An untrusted agent writing <project>/my-skill/data as a hardlink to ~/.ssh/id_rsa would have shipped the SSH key content to the (possibly public) library on the next skillctl push or detect.

Patches

Fixed in v0.1.3:

  • InstalledSkill::validate rejects any source_sha that isn't 40–64 hex characters.
  • fs_util::copy_dir_all only allows regular files and directories; FIFO / socket / device / other special files are rejected with AppError::Config.
  • commands::add::resolve_destination rejects .. unconditionally and rejects absolute paths in non-interactive / --json mode.
  • New src/sanitize.rs module: validate_identifier (strict, no control bytes / newlines / ESC, used for skill name + individual tags) and validate_message_safe (lenient, allows \n + \t, rejects \r + DEL + C0/C1 controls, used for description and --message). Wired at the skill::discover and read_tags boundaries so poisoned skills are dropped silently and poisoned descriptions/tags are stripped from otherwise-valid skills.
  • fs_util::copy_dir_all checks metadata.nlink() > 1 on regular files (Unix) and refuses hardlinked content.

All checks are lexical or single-syscall (symlink_metadata, metadata). No canonicalize, no TOCTOU windows. 23 new unit + integration tests cover each rejection class; cargo test: 95 pass; clippy clean; cargo audit clean.

Workarounds

Upgrade to v0.1.3. Pre-patch mitigations are awkward but possible:

  • Audit every .skills.toml source_sha field before running skillctl pull / push / detect.
  • Audit library content for FIFO / device files and hardlinks before running skillctl add.
  • Never invoke skillctl add with attacker-controllable --dest values in agent / CI contexts.
  • Never use --message with attacker-controlled content.

Credit

The findings were surfaced by a maintainer-led multi-angle audit (6 parallel sub-agents, one per threat-model dimension) following the firebaguette audit that motivated v0.1.2. The methodology (cross-agent convergence to identify the most exploitable items) is documented in the project's internal decisions log; the strongest signal was the four-of-six independent convergence on the source_sha vector.

References

Severity

High

CVE ID

No known CVE

Weaknesses

No CWEs