SkillSpector

Security scanner for AI agent skills. Detect vulnerabilities, malicious patterns, and security risks before installing agent skills.

Overview

AI agent skills (used by Claude Code, Codex CLI, Gemini CLI, etc.) execute with implicit trust and minimal vetting. Research shows that 26.1% of skills contain vulnerabilities and 5.2% show likely malicious intent.

SkillSpector helps you answer: “Is this skill safe to install?”

SkillSpector is part of the NVIDIA Verified Skills pipeline, which scans, evaluates, and signs agent skills before publication. Skills that pass are published to the NVIDIA skills catalog.

Documentation

• Scan agent skills before installation — Hosted guide: when to scan, how to read a report, and how to gate installs.
• Development guide — Architecture, package layout, and how to extend the analyzer pipeline.
• Analysis resource bounds — Fail-closed bundle, parser, nested-artifact, ledger, and finding ceilings.
• Pi extension — Install SkillSpector as a Pi tool for scanning skills from inside agent sessions.

Features

• Multi-format input: Scan Git repos, URLs, zip files, directories, or single files
• 71 vulnerability patterns across 17 categories: prompt injection, data exfiltration, privilege escalation, supply chain, excessive agency, output handling, system prompt leakage, memory poisoning, tool misuse, rogue agent, anti-refusal, trigger abuse, dangerous code (AST), taint tracking, YARA signatures, MCP least privilege, and MCP tool poisoning
• Two-stage analysis: Fast static analysis + optional LLM semantic evaluation
• Live vulnerability lookups: SC4 queries OSV.dev for real-time CVE data with automatic offline fallback
• Multiple output formats: Terminal, JSON, Markdown, and SARIF reports
• Risk scoring: 0-100 score with severity labels and clear recommendations
• Baseline / false-positive suppression: Accept known findings via a glob-rule or fingerprint baseline so re-scans surface only new issues (docs)

Quick Start

Installation

Open-source software notice: This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use.

Create and activate a virtual environment first (all make targets assume the venv is active). Use uv or pip; the Makefile uses uv if available, otherwise pip.

Quick install with uv (CLI-only):

uv tool install git+https://github.com/NVIDIA/skillspector.git
# Update later: uv tool update skillspector

If you plan to run skillspector mcp, install the MCP extra at install time:

uv tool install 'skillspector[mcp] @ git+https://github.com/NVIDIA/skillspector.git'

From source:

# Clone the repository
git clone https://github.com/NVIDIA/skillspector.git
cd skillspector

# Create and activate virtual environment
uv venv .venv && source .venv/bin/activate
# or: python3 -m venv .venv && source .venv/bin/activate

# Install for production use
make install

# Or install with development dependencies
make install-dev

Docker (no Python required)

Run SkillSpector without installing Python by building it locally from the included Dockerfile. The image is based on the Docker Official Python 3.12-slim-bookworm image.

Build the image:

make docker-build
# or: docker build -t skillspector .

Scan a local directory by mounting your current directory into /scan, the container’s working directory:

docker run --rm -v "$PWD:/scan" skillspector scan ./my-skill/ --no-llm

Scan with LLM analysis by passing credentials with a local .env file:

cat > .env <<'EOF'
SKILLSPECTOR_PROVIDER=anthropic
ANTHROPIC_API_KEY=sk-ant-...
EOF

docker run --rm \
  -v "$PWD:/scan" \
  --env-file .env \
  skillspector scan ./my-skill/

Or pass credentials directly from your shell environment:

docker run --rm \
  -v "$PWD:/scan" \
  -e SKILLSPECTOR_PROVIDER=anthropic \
  -e ANTHROPIC_API_KEY="$ANTHROPIC_API_KEY" \
  skillspector scan ./my-skill/

Write a report to the host filesystem by writing to the mounted directory:

docker run --rm \
  -v "$PWD:/scan" \
  skillspector scan ./my-skill/ --no-llm --format json --output report.json

Optional alias for repeated static scans:

alias skillspector-docker='docker run --rm -v "$PWD:/scan" skillspector'
skillspector-docker scan ./my-skill/ --no-llm

Basic Usage

# Scan a local skill directory
skillspector scan ./my-skill/

# Scan a single SKILL.md file
skillspector scan ./SKILL.md

# Scan a Git repository
skillspector scan https://github.com/user/my-skill

# Scan a zip file
skillspector scan ./my-skill.zip

Size limits

SkillSpector enforces two independent caps on remote and archive inputs to bound the impact of oversized downloads and zip bombs:

• Per-ingest cap: INGEST_MAX_BYTES (100 MiB) — applied to streamed URL downloads, total uncompressed size of zip archives, and post-clone disk usage of Git repos.
• Zip member cap: INGEST_MAX_ZIP_MEMBERS (10,000) — caps the number of entries in a single zip.

Note that the per-file 1 MB analysis cap (MAX_FILE_BYTES) is a separate, downstream limit: it bounds what individual analyzers will read out of an already-ingested directory. The ingest caps above bound how much content can land on disk in the first place. A breach of either ingest cap fails closed with an IngestLimitExceededError.

Output Formats

# Terminal output (default) - pretty formatted
skillspector scan ./my-skill/

# JSON output - machine readable
skillspector scan ./my-skill/ --format json --output report.json

# Markdown output - for documentation
skillspector scan ./my-skill/ --format markdown --output report.md

# SARIF output - for CI/CD integration and IDE tooling
skillspector scan ./my-skill/ --format sarif --output report.sarif

Batch Scanning

Scan entire directories of skills in parallel from contrib/batch_scan/:

python -m contrib.batch_scan.batch_scan ./my-skills/ --no-llm
python -m contrib.batch_scan.batch_scan ./my-skills/ --workers 20 -f json -o report.json
python -m contrib.batch_scan.batch_scan ./tests/fixtures/ -f terminal --workers 20

Supports multilingual detection (zh/ja/ko) and terminal/JSON/Markdown output.

For LLM scans with higher concurrency, configure multiple API keys following .env.example — the pool improves throughput and resilience, provided the keys don’t share an account-level rate limit.

See the contrib guide for details.

Note on LLM support: The default configuration targets DeepSeek as the cheapest public option. DeepSeek-Chat is expected to sunset, and the contributor does not have hardware to test against local models. The batch scanner was originally tested with OpenAI-compatible endpoints — DeepSeek’s lack of structured-output support required manual JSON-parsing patches. If you can contribute a more universal backend (Ollama, vLLM, or a different provider), PRs are very welcome.

Suppressing False Positives (baseline)

Suppress known/accepted findings so the risk score reflects only un-triaged issues and re-scans surface only new findings. See the suppression guide for the full reference.

# Accept all current findings into a baseline (run once), then commit it.
skillspector baseline ./my-skill/ -o .skillspector-baseline.yaml

# Scan against the baseline — only NEW findings are reported and scored.
skillspector scan ./my-skill/ --baseline .skillspector-baseline.yaml

# Review what was suppressed (still excluded from the score).
skillspector scan ./my-skill/ --baseline .skillspector-baseline.yaml --show-suppressed

A baseline can also use drift-tolerant glob rules (by rule id, file path, or message) — see .skillspector-baseline.example.yaml. Exact fingerprint baselines are evidence-bound: changing the scanned source or SkillSpector version keeps the finding active until it is reviewed again. When a selected baseline or baseline output is stored inside the skill directory, SkillSpector excludes that exact file from content analysis so its suppression text cannot create findings or enter regenerated fingerprints; sibling files remain in normal scan scope.

LLM Analysis

For the best results, configure an OpenAI-compatible LLM endpoint for semantic analysis. Pick a provider with SKILLSPECTOR_PROVIDER; hosted providers ship bundled default models, while CLI providers fall back to the local runtime’s default model unless SKILLSPECTOR_MODEL is set. SkillSpector also works against local OpenAI-compatible servers (Ollama, vLLM, llama.cpp) and managed inference gateways.

Provider (SKILLSPECTOR_PROVIDER)	Credential env var	Endpoint	Default model
openai	OPENAI_API_KEY (+ optional OPENAI_BASE_URL)	api.openai.com (or any OpenAI-compatible URL)	gpt-5.4
anthropic	ANTHROPIC_API_KEY	api.anthropic.com	claude-opus-4-6
anthropic_proxy	ANTHROPIC_PROXY_API_KEY + ANTHROPIC_PROXY_ENDPOINT_URL	Any Vertex-style raw-predict proxy	claude-sonnet-4-6
bedrock	AWS_PROFILE (optional) + AWS_REGION — SigV4 via boto3	AWS Bedrock Runtime	us.anthropic.claude-sonnet-4-6-20250915-v1:0
nv_build	NVIDIA_INFERENCE_KEY	build.nvidia.com	deepseek-ai/deepseek-v4-flash
claude_cli	(none — uses local CLI auth)	local claude binary	local Claude runtime fallback, or SKILLSPECTOR_MODEL
codex_cli	(none — uses local CLI auth)	local codex binary	local Codex runtime fallback, or SKILLSPECTOR_MODEL

# Stock OpenAI
export SKILLSPECTOR_PROVIDER=openai
export OPENAI_API_KEY=sk-...
skillspector scan ./my-skill/

# Anthropic
export SKILLSPECTOR_PROVIDER=anthropic
export ANTHROPIC_API_KEY=sk-ant-...
skillspector scan ./my-skill/

# Anthropic via Vertex-style proxy (corporate gateways, GCP Vertex AI)
export SKILLSPECTOR_PROVIDER=anthropic_proxy
export ANTHROPIC_PROXY_ENDPOINT_URL=https://my-gateway.example.com/models/claude-sonnet-4-6:streamRawPredict
export ANTHROPIC_PROXY_API_KEY=your-bearer-token
export SKILLSPECTOR_MODEL=claude-sonnet-4-6
skillspector scan ./my-skill/

# AWS Bedrock (Claude via SigV4)
export SKILLSPECTOR_PROVIDER=bedrock
# Optional: select an AWS named profile. When unset, the standard
# boto3 credential chain (env vars, instance metadata, SSO, etc.) resolves.
# export AWS_PROFILE=my-profile
export AWS_REGION=us-west-2  # default if unset
# Default model: us.anthropic.claude-sonnet-4-6-20250915-v1:0
# Override with any Bedrock model ID, cross-region inference-profile
# ID, or your own application-inference-profile ARN:
# export SKILLSPECTOR_MODEL=us.anthropic.claude-opus-4-6-20250915-v1:0
skillspector scan ./my-skill/

# NVIDIA build.nvidia.com
export SKILLSPECTOR_PROVIDER=nv_build
export NVIDIA_INFERENCE_KEY=nvapi-...
skillspector scan ./my-skill/

# Local Claude CLI — no API key; uses your existing `claude auth login` session
# Requires: claude CLI installed and authenticated (claude auth login)
export SKILLSPECTOR_PROVIDER=claude_cli
# Uses the local Claude CLI runtime fallback unless SKILLSPECTOR_MODEL is set.
# export SKILLSPECTOR_MODEL=claude-sonnet-4-6
skillspector scan ./my-skill/

# Local Codex CLI — no API key; uses your existing `codex login` session
# Requires: codex CLI installed and authenticated
export SKILLSPECTOR_PROVIDER=codex_cli
skillspector scan ./my-skill/

# Local Ollama or any OpenAI-compatible endpoint
export SKILLSPECTOR_PROVIDER=openai
export OPENAI_API_KEY=ollama
export OPENAI_BASE_URL=http://localhost:11434/v1
export SKILLSPECTOR_MODEL=llama3.1:8b
skillspector scan ./my-skill/

# Override the provider's default model
export SKILLSPECTOR_MODEL=gpt-5.2
skillspector scan ./my-skill/

# Skip LLM analysis (faster, static analysis only)
skillspector scan ./my-skill/ --no-llm

MCP Server

Run SkillSpector as a Model Context Protocol server so any MCP-capable agent (Claude Code, Codex CLI, Gemini CLI) or remote runtime can call scanning as a tool and gate skill/MCP installs on the result — turning SkillSpector into a runtime guardrail instead of an out-of-band audit step.

skillspector mcp requires skillspector[mcp].

# Install, or reinstall if you already used the CLI-only path
uv tool install --force 'skillspector[mcp] @ git+https://github.com/NVIDIA/skillspector.git'

# FastMCP stdio transport for local CLI agents
skillspector mcp

# streamable HTTP/SSE transport for remote / A2A callers
skillspector mcp --transport http --host 127.0.0.1 --port 8000

The stdio transport is the current FastMCP path for local CLI agents, and the initialize hang reported in issue #199 still applies there.

The server exposes a single tool:

• scan_skill(target, use_llm=true, output_format="json") — scans a Git URL, file URL, .zip, .md file, or directory and returns a structured verdict: risk_score (0-100), severity, recommendation, safe_to_install, and findings. It also reports llm_used / scan_mode so a low score from a static-only scan is never mistaken for a clean full scan.

Register it with Claude Code via:

claude mcp add skillspector -- skillspector mcp

Security — HTTP transport trust model

The HTTP transport ships without authentication. Any caller that can reach the port can invoke scan_skill. Over stdio or 127.0.0.1 this is the same trust boundary as the CLI. If you bind to a routable interface:

• Sit the server behind an authenticating reverse proxy (e.g. nginx + mTLS) before exposing it externally.
• Local paths and file:// URLs are automatically rejected over HTTP to prevent unauthenticated callers from reading arbitrary host files. Only remote Git and .zip URLs are accepted.

Vulnerability Patterns

SkillSpector detects 71 vulnerability patterns across 17 categories:

Prompt Injection (6 patterns)

ID	Pattern	Severity	Description
P1	Instruction Override	HIGH	Commands to ignore safety constraints
P2	Hidden Instructions	HIGH	Malicious directives in comments/invisible text
P3	Exfiltration Commands	HIGH	Instructions to transmit context externally
P4	Behavior Manipulation	MEDIUM	Subtle instructions altering agent decisions
P5	Harmful Content	CRITICAL	Instructions that could cause physical harm
P9	Whitespace Padding	MEDIUM	Large whitespace padding hiding instructions below/beside the visible area

Anti-Refusal (3 patterns)

ID	Pattern	Severity	Description
AR1	Refusal Suppression	HIGH	Instructions to never refuse or always comply (e.g. “never refuse”, “always comply”)
AR2	Disclaimer Suppression	HIGH	Instructions to omit warnings, disclaimers, or ethical commentary (e.g. “no disclaimers”, “do not moralize”)
AR3	Safety Policy Nullification	HIGH	Jailbreak framing that nullifies guardrails (e.g. “you have no restrictions”, “ignore your guidelines”, “do anything now”)

Data Exfiltration (4 patterns)

ID	Pattern	Severity	Description
E1	External Transmission	MEDIUM	Sending data to external URLs
E2	Env Variable Harvesting	HIGH	Enumerating, copying, or searching environment data to collect secrets
E3	File System Enumeration	MEDIUM	Scanning directories for sensitive files
E4	Context Leakage	HIGH	Transmitting conversation context externally

Privilege Escalation (3 patterns)

ID	Pattern	Severity	Description
PE1	Excessive Permissions	LOW	Requesting access beyond stated functionality
PE2	Sudo/Root Execution	MEDIUM	Invoking elevated system privileges
PE3	Credential Access	HIGH	Reading SSH keys, tokens, passwords

Supply Chain (9+ patterns)

ID	Pattern	Severity	Description
SC1	Unpinned Dependencies	LOW	No version constraints on packages
SC2	External Script Fetching	HIGH	curl | bash and remote code execution
SC3	Obfuscated Code	HIGH	Base64/hex encoded execution
SC4	Known Vulnerable Dependencies	HIGH	Dependencies with known CVEs (live OSV.dev lookup)
SC5	Abandoned Dependencies	MEDIUM	Unmaintained packages without security updates
SC6	Typosquatting	HIGH	Package names similar to popular packages
SC8	Shipped Python Bytecode	HIGH	__pycache__ / .pyc present (discovery skips; malicious bytecode bypass)
SC9	Concealed Executable Artifact	HIGH	Executable nested in a document container or hidden/disguised artifact

Excessive Agency (5 patterns)

ID	Pattern	Severity	Description
EA1	Unrestricted Tool Access	HIGH	Unfettered tool access without constraints
EA2	Autonomous Decision Making	HIGH	High-impact decisions without human-in-the-loop
EA3	Scope Creep	MEDIUM	Capabilities extending beyond stated purpose
EA4	Unbounded Resource Access	MEDIUM	No rate limits or quotas on resource consumption
EA5	External Model or Provider Selection	MEDIUM/HIGH	Model/provider pins or coding-CLI shell-outs that can switch billing accounts

Output Handling (3 patterns)

ID	Pattern	Severity	Description
OH1	Unvalidated Output Injection	HIGH	Model output used without sanitization
OH2	Cross-Context Output	MEDIUM	Output flows across trust boundaries without validation
OH3	Unbounded Output	MEDIUM	No limits on output size or generation rate

System Prompt Leakage (3 patterns)

ID	Pattern	Severity	Description
P6	Direct Leakage	HIGH	Instructions that expose system prompts or internal rules
P7	Indirect Extraction	MEDIUM	Extraction via rephrasing, translation, or side-channels
P8	Tool-Based Exfiltration	HIGH	System prompts exfiltrated via file writes or network requests

Memory Poisoning (3 patterns)

ID	Pattern	Severity	Description
MP1	Persistent Context Injection	HIGH	Content designed to persist across interactions
MP2	Context Window Stuffing	MEDIUM	Filler content displacing safety constraints
MP3	Memory Manipulation	HIGH	Tampering with agent memory or stored state

Tool Misuse (3 patterns)

ID	Pattern	Severity	Description
TM1	Tool Parameter Abuse	HIGH	Crafted parameters for unintended behavior (shell=True, —force)
TM2	Chaining Abuse	HIGH	Tool chains that bypass individual safety checks
TM3	Unsafe Defaults	MEDIUM	Overly permissive defaults (disabled TLS, no auth)

Rogue Agent (2 patterns)

ID	Pattern	Severity	Description
RA1	Self-Modification	CRITICAL	Modifying own code or configuration at runtime
RA2	Session Persistence	HIGH	Unauthorized persistence via cron jobs or startup scripts

Trigger Abuse (3 patterns)

ID	Pattern	Severity	Description
TR1	Overly Broad Trigger	MEDIUM	Trigger patterns matching common words
TR2	Shadow Command Trigger	HIGH	Triggers that shadow built-in commands or other skills
TR3	Keyword Baiting Trigger	MEDIUM	Generic triggers designed to maximize activation

Behavioral AST (9 patterns)

ID	Pattern	Severity	Description
AST1	exec() Call	CRITICAL	Direct exec() enabling arbitrary code execution
AST2	eval() Call	HIGH	Direct eval() evaluating arbitrary expressions
AST3	Dynamic Import	HIGH	__import__() loading arbitrary modules at runtime
AST4	subprocess Call	HIGH	External command execution via subprocess
AST5	os.system / exec-family	HIGH	Shell commands via os module
AST6	compile() Call	MEDIUM	Code object creation from strings
AST7	Dynamic getattr()	MEDIUM	Arbitrary attribute access with non-literal names
AST8	Dangerous Execution Chain	CRITICAL	exec/eval combined with dynamic source (network, encoded data)
AST9	Reflective getattr() Sink	HIGH	Reflective exec via getattr(os,'system') / getattr(builtins,'exec') that evades AST1/AST5

Taint Tracking (5 patterns)

ID	Pattern	Severity	Description
TT1	Direct Taint Flow	HIGH	Data flows directly from a source to a sink without sanitization
TT2	Variable-Mediated Taint Flow	MEDIUM	Data flows from source to sink through intermediate variables
TT3	Credential Exfiltration Chain	CRITICAL	Credentials (env vars, secrets) flow to network output sinks
TT4	File Read to Network Exfiltration	HIGH	File contents flow to network output sinks
TT5	External Input to Code Execution	CRITICAL	Network or user input flows to exec/eval/subprocess sinks

YARA Signatures (4 patterns)

ID	Pattern	Severity	Description
YR1	Malware Match	CRITICAL	YARA rule match for known malware signatures
YR2	Webshell Match	CRITICAL	YARA rule match for webshell patterns
YR3	Cryptominer Match	HIGH	YARA rule match for crypto mining indicators
YR4	Hack Tool / Exploit Match	HIGH	YARA rule match for hack tools or exploit code

MCP Least Privilege (4 patterns)

ID	Pattern	Severity	Description
LP1	Underdeclared Capability	HIGH	Code uses capabilities not listed in declared permissions
LP2	Wildcard Permission	MEDIUM	Permission list contains wildcards (*, all, full, any)
LP3	Missing Permission Declaration	MEDIUM	No permissions field but code has detectable capabilities
LP4	Overdeclared Permission	LOW	Permission declared but no corresponding code capability found