Parry-guard
Prompt injection scanner for Claude Code hooks. Scans tool inputs and outputs for injection attacks, secrets, and data exfiltration attempts.
Early development — this tool is under active development and may have bugs or false positives. Tested on linux/macOS.
Prerequisites
The ML models are gated on HuggingFace. Before installing:
- Create an account at huggingface.co
- Accept the DeBERTa v3 license (required for all modes)
- For
fullmode: also accept the Llama Prompt Guard 2 license (Meta approval required) - Create an access token at huggingface.co/settings/tokens
Usage
Add to ~/.claude/settings.json:
With uvx:
With rvx:
With parry-guard on PATH (via Nix, cargo install, or release binary):
From source:
# Default (ONNX backend - statically linked, 5-6x faster than Candle)
# Candle backend (pure Rust, no native deps, portable)
Nix (home-manager)
# flake.nix
{
inputs.parry.url = "github:vaporif/parry";
outputs = { parry, ... }: {
# pass parry to your home-manager config via extraSpecialArgs, overlays, etc.
};
}
# home-manager module
{ inputs, pkgs, config, ... }: {
imports = [ inputs.parry.homeManagerModules.default ];
programs.parry-guard = {
enable = true;
package = inputs.parry.packages.${pkgs.system}.default; # onnx (default)
# package = inputs.parry.packages.${pkgs.system}.candle; # candle (pure Rust, portable, ~5-6x slower)
hfTokenFile = config.sops.secrets.hf-token.path;
ignoreDirs = [ "/home/user/repos/trusted" ];
# askOnNewProject = true; # Ask before monitoring new projects (default: auto-monitor)
# claudeMdThreshold = 0.9; # ML threshold for CLAUDE.md scanning (default 0.9)
# scanMode = "full"; # fast (default) | full | custom
# Custom models (auto-sets scanMode to "custom")
# models = [
# { repo = "ProtectAI/deberta-v3-small-prompt-injection-v2"; }
# { repo = "meta-llama/Llama-Prompt-Guard-2-86M"; threshold = 0.5; }
# ];
};
}
Setup
1. Configure HuggingFace token
One of (first match wins):
# direct value
# file path
# or place token at /run/secrets/hf-token-scan-injection
The daemon auto-starts on first scan, downloads the model on first run, and idles out after 30 minutes.
Note (non-Nix users): The Nix home-manager module wraps the binary with all config baked in via env vars. Without Nix, set env vars in your shell profile (e.g.
HF_TOKEN,PARRY_IGNORE_DIRS,PARRY_SCAN_MODE) — the hook command inherits them. Alternatively, pass flags directly in the hook command:parry-guard --hf-token-path ~/.hf-token --ignore-dirs /home/user/trusted hook. See Config for all options.
Project scanning
By default, parry auto-monitors every new project — scanning is active from the first session with no prompt. To opt out of a specific repo, run parry-guard ignore <path>.
To restore the old ask-first behavior, set PARRY_ASK_ON_NEW_PROJECT=true (or askOnNewProject = true in Nix). See docs/opt-in-flow.md for the full flow.
| Command | Description |
|---|---|
parry-guard monitor [path] |
Enable scanning for a repo |
parry-guard ignore [path] |
Disable scanning for a repo |
parry-guard reset [path] |
Clear state and caches, back to unknown |
parry-guard status [path] |
Show current repo state and findings |
parry-guard repos |
List all known repos and their states |
All commands default to the current directory if path is omitted.
What each hook does
- PreToolUse: 7-layer security — ignored/unknown repo skip, taint enforcement, CLAUDE.md scanning, exfil blocking, destructive operation detection, sensitive path blocking, input content injection scanning (Write/Edit/Bash/MCP tools)
- PostToolUse: Scans tool output for injection/secrets, auto-taints project on detection
- UserPromptSubmit: Audits
.claude/directory for dangerous permissions, injected commands, hook scripts
Daemon & Cache
The daemon keeps ML models in memory and can be run standalone with parry-guard serve --idle-timeout 1800. Hook calls auto-start it if not running.
Scan results are cached in ~/.parry-guard/scan-cache.redb (30-day TTL, ~8ms cache hits vs ~70ms+ inference). Cache is shared across projects and pruned hourly.
Detection Layers
Multi-stage, fail-closed (if unsure, treat as unsafe):
- Unicode — invisible characters (PUA, unassigned codepoints), homoglyphs, RTL overrides
- Substring — Aho-Corasick matching for known injection phrases
- Secrets — 40+ regex patterns for credentials (AWS, GitHub/GitLab, cloud providers, database URIs, private keys, etc.)
- ML Classification — DeBERTa v3 transformer with text chunking (256 chars, 25 overlap) and head+tail strategy for long texts. Configurable threshold (default 0.7).
- Bash Exfiltration — tree-sitter AST analysis for data exfil: network sinks, command substitution, obfuscation (base64, hex, ROT13), DNS tunneling, cloud storage, 60+ sensitive paths, 40+ exfil domains
- Script Exfiltration — same source→sink analysis for script files across 16 languages
Scan modes
| Mode | Models | Latency/chunk | Backend |
|---|---|---|---|
fast (default) |
DeBERTa v3 | ~50-70ms | any |
full |
DeBERTa v3 + Llama Prompt Guard 2 | ~1.5s | candle only |
custom |
User-defined (~/.config/parry-guard/models.toml) |
varies | any |
Use fast for interactive workflows; full for high-security or batch scanning (parry-guard diff --full). The two models cover different blind spots — DeBERTa v3 catches common injection patterns while Llama Prompt Guard 2 is better at subtle, context-dependent attacks (role-play jailbreaks, indirect injections). Running both as an OR ensemble reduces missed attacks at ~20x higher latency per chunk.
Note:
fullmode requires thecandlebackend — Llama Prompt Guard 2 does not ship an ONNX export. Build with--features candle --no-default-featuresto usefullmode.
Config
Global flags
| Flag | Env | Default | Description |
|---|---|---|---|
--threshold |
PARRY_THRESHOLD |
0.7 | ML detection threshold (0.0–1.0) |
--claude-md-threshold |
PARRY_CLAUDE_MD_THRESHOLD |
0.9 | ML threshold for CLAUDE.md scanning (0.0–1.0) |
--scan-mode |
PARRY_SCAN_MODE |
fast | ML scan mode: fast, full, custom |
--hf-token |
HF_TOKEN |
— | HuggingFace token (direct value) |
--hf-token-path |
HF_TOKEN_PATH |
/run/secrets/hf-token-scan-injection |
HuggingFace token file |
--ask-on-new-project |
PARRY_ASK_ON_NEW_PROJECT |
false | Ask before monitoring new projects (default: auto-monitor) |
--ignore-dirs |
PARRY_IGNORE_DIRS |
— | Parent directories to ignore — all repos under these paths are skipped (comma-separated) |
Subcommand flags
| Flag | Env | Default | Description |
|---|---|---|---|
serve --idle-timeout |
PARRY_IDLE_TIMEOUT |
1800 | Daemon idle timeout in seconds |
diff --full |
— | false | Use ML scan instead of fast-only |
diff -e, --extensions |
— | — | Filter by file extension (comma-separated) |
Env-only
| Env | Default | Description |
|---|---|---|
PARRY_LOG |
warn | Tracing filter (trace, debug, info, warn, error) |
PARRY_LOG_FILE |
~/.parry-guard/parry-guard.log |
Override log file path |
Custom patterns: ~/.config/parry-guard/patterns.toml (add/remove sensitive paths, exfil domains, secret patterns).
Custom models: ~/.config/parry-guard/models.toml (used with --scan-mode custom, see examples/models.toml).
ML Backends
One backend is always required (enforced at compile time). Nix default is ONNX (x86_64-linux, aarch64-linux, aarch64-darwin). Use candle package on other platforms.
| Feature | Description |
|---|---|
onnx-fetch |
ONNX, statically linked (downloads ORT at build time). Default. |
candle |
Pure Rust ML. Portable, no native deps. ~5-6x slower. |
onnx |
ONNX, you provide ORT_DYLIB_PATH. |
onnx-coreml |
(experimental) ONNX with CoreML on Apple Silicon. |
# Build with Candle instead of ONNX
Performance
Apple Silicon, release build, fast mode (DeBERTa v3 only). Candle is 5-6x slower than ONNX (default). Run just bench-candle / just bench-onnx to reproduce (requires HF_TOKEN).
| Scenario | ONNX (default) | Candle |
|---|---|---|
| Short text (1 chunk) | ~10ms | ~61ms |
| Medium text (2 chunks) | ~32ms | ~160ms |
| Long text (6 chunks) | ~136ms | ~683ms |
| Cold start (daemon + model load) | ~580ms | ~1s |
| Fast-scan short-circuit | ~7ms | ~7ms |
| Cached result | ~8ms | ~8ms |
Llama Prompt Guard 2 does not ship an ONNX export, so
fullmode requires thecandlebackend.
Contributing
See CONTRIBUTING.md for development setup, commands, and contribution guidelines.
Credits
- ML model: ProtectAI/deberta-v3-small-prompt-injection-v2
- Same model used by LLM Guard
- Exfil patterns: Inspired by GuardDog (Datadog's malicious package scanner)
- Full scan mode optionally uses Llama Prompt Guard 2 86M by Meta, licensed under the Llama 4 Community License. Built with Llama.
License
MIT
Llama Prompt Guard 2 (used in full scan mode) is licensed separately under the Llama 4 Community License. See LICENSE-LLAMA.