parry-guard 0.1.4

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

Parry-guard

Prompt injection scanner for Claude Code hooks. Catches injection attacks, leaked secrets, and data exfiltration in tool inputs and outputs.

Early development - bugs and false positives happen. Tested on Linux and macOS.

Prerequisites

The ML models are gated on HuggingFace, so you need to accept licenses 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 bin

# Candle backend (pure Rust, no native deps, portable)
cargo install --path bin --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-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

HuggingFace token

Provide your token via 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 starts on the first scan, downloads the model on the first run, and shuts down after 30 minutes idle. Non-Nix users: set env vars in your shell profile or pass flags directly (see Config).

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 get 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	Effect
parry-guard monitor [path]	Turn on scanning for a repo
parry-guard ignore [path]	Turn off 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

path defaults to the current directory.

What each hook does

PreToolUse runs 7 checks in order, stopping at the first match: ignored/unknown repo skip, taint enforcement, CLAUDE.md scanning, exfil blocking, destructive operation detection, sensitive path blocking, and input content injection scanning (Write/Edit/Bash/MCP tools).

PostToolUse scans tool output for injection and secrets. If it finds something, it auto-taints the project.

UserPromptSubmit audits your .claude/ directory for dangerous permissions, injected commands, and hook scripts.

Daemon and cache

You can run the daemon standalone with parry-guard serve --idle-timeout 1800. Hook calls start it automatically if it isn't running.

Scan results are cached in ~/.parry-guard/scan-cache.redb with a 30-day TTL. Cache hits take about 8ms vs 70ms+ for inference. The cache is shared across projects and pruned hourly.

Detection layers

The scanner is fail-closed: if it can't tell whether something is safe, it treats it 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 a head+tail strategy for long texts. Threshold defaults to 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-to-sink analysis for script files across 16 languages

Scan modes

Mode	Models	Latency per 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 work and full for high security or batch scanning (parry-guard diff --full). The two models have different blind spots — DeBERTa v3 is good at common injection patterns, while Llama Prompt Guard 2 is better at subtle stuff like role-play jailbreaks and indirect injections. Running both as an OR ensemble means fewer missed attacks, but at roughly 20x higher latency per chunk.

Note: full mode needs the candle backend because Llama Prompt Guard 2 doesn't have an ONNX export. Build with --features candle --no-default-features.

Config

Global flags

Flag	Env	Default	Effect
--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, comma-separated. All repos under these paths get skipped.

Subcommand flags

Flag	Env	Default	Effect
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)

Environment-only variables

Env	Default	Effect
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 defaults to ONNX on x86_64-linux, aarch64-linux, and aarch64-darwin. Use the candle package on other platforms.

Feature
onnx-fetch	ONNX, statically linked (downloads ORT at build time). Default.
candle	Pure Rust ML. Portable, no native deps. About 5-6x slower.
onnx	ONNX, you provide ORT_DYLIB_PATH.
onnx-coreml	(experimental) ONNX with CoreML on Apple Silicon.

Performance

Apple Silicon, release build, fast mode (DeBERTa v3 only). Candle is about 5-6x slower than ONNX. 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

Contributing

See CONTRIBUTING.md.

Credits

• ML model: ProtectAI/deberta-v3-small-prompt-injection-v2, also 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.