parry-guard 0.1.0a1

Prompt injection scanner CLI - substring, unicode, secrets, and ML detection

Parry

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:

1. Create an account at huggingface.co
2. Accept the DeBERTa v3 license (required for all modes)
3. For full mode: also accept the Llama Prompt Guard 2 license (Meta approval required)
4. Create an access token at huggingface.co/settings/tokens

Usage

Add to ~/.claude/settings.json:

With uvx:

{
  "hooks": {
    "PreToolUse": [{ "command": "uvx parry-guard hook", "timeout": 1000 }],
    "PostToolUse": [{ "command": "uvx parry-guard hook", "timeout": 5000 }],
    "UserPromptSubmit": [{ "command": "uvx parry-guard hook", "timeout": 2000 }]
  }
}

With rvx:

{
  "hooks": {
    "PreToolUse": [{ "command": "rvx parry-guard hook", "timeout": 1000 }],
    "PostToolUse": [{ "command": "rvx parry-guard hook", "timeout": 5000 }],
    "UserPromptSubmit": [{ "command": "rvx parry-guard hook", "timeout": 2000 }]
  }
}

With parry-guard on PATH (via Nix, cargo install, or release binary):

{
  "hooks": {
    "PreToolUse": [{ "command": "parry-guard hook", "timeout": 1000 }],
    "PostToolUse": [{ "command": "parry-guard hook", "timeout": 5000 }],
    "UserPromptSubmit": [{ "command": "parry-guard hook", "timeout": 2000 }]
  }
}

Other installation methods

From source:

# Default (ONNX backend - statically linked, 5-6x faster than Candle)
cargo install --path crates/cli

# Candle backend (pure Rust, no native deps, portable)
cargo install --path crates/cli --no-default-features --features candle

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 = {
    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;
    ignorePaths = [ "/home/user/repos/parry" ];
    # 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):

export HF_TOKEN="hf_..."                          # direct value
export HF_TOKEN_PATH="/path/to/token"              # 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_PATHS, PARRY_SCAN_MODE) — the hook command inherits them. Alternatively, pass flags directly in the hook command: parry-guard --hf-token-path ~/.hf-token --ignore-path /home/user/safe hook. See Config for all options.

What each hook does

• PreToolUse: 5-layer security — taint enforcement, CLAUDE.md scanning, exfil blocking, 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):

1. Unicode — invisible characters (PUA, unassigned codepoints), homoglyphs, RTL overrides
2. Substring — Aho-Corasick matching for known injection phrases
3. Secrets — 40+ regex patterns for credentials (AWS, GitHub/GitLab, cloud providers, database URIs, private keys, etc.)
4. ML Classification — DeBERTa v3 transformer with text chunking (256 chars, 25 overlap) and head+tail strategy for long texts. Configurable threshold (default 0.7).
5. 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
6. 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: full mode requires the candle backend — Llama Prompt Guard 2 does not ship an ONNX export. Build with --features candle --no-default-features to use full mode.

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
--ignore-path	PARRY_IGNORE_PATHS	—	Paths to skip scanning (comma-separated / repeatable)

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
cargo build --no-default-features --features candle

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 full mode requires the candle backend.

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.