SkillSpector

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. In the 31,132-skill analyzed subset of the research dataset, 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.
• Security policy — Report suspected vulnerabilities privately to NVIDIA PSIRT.
• 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.
• OpenCode extension — Install SkillSpector as an OpenCode tool and /skillspector command 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

To contribute, follow the Development setup for a supported Python interpreter and development dependencies.

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 --user "$(id -u):$(id -g)" \
  -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

Recursive Collection Scanning

--recursive scans immediate subdirectories containing SKILL.md as independent skills, with a default aggregate limit of 32 skills. To raise or lower it:

skillspector scan ./my-skills/ --recursive --max-skills 100 --no-llm --format json

--max-skills requires a local recursive collection and a positive integer; 0 does not mean unlimited. Discovery, byte, finding-record, time and report-size budgets still apply, so increasing the count does not guarantee complete coverage. When the count limit is reached, the CLI names the limit and suggests adjusting it or scanning sub-skills separately. Omitted skills remain visible in the aggregate report and coverage stays incomplete. Use --fail-on-incomplete in CI to fail on incomplete coverage; the default exit policy is unchanged.

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.

Comparing MCP Registry snapshots

Start with raw registry payload captures (registry-before.json and registry-after.json). Save a scan report as previous.json, then compare a later scan against that generated local report:

skillspector scan registry-before.json --mcp-registry --format json --output previous.json
skillspector scan registry-after.json --mcp-registry --format json \
  --mcp-registry-compare previous.json --output compared.json

The optional comparison object lists added and removed server identities, changed normalized fields with their previous and current values, and an unchanged_count. Identity is the server name and version, so a new version appears as an addition and the old version as a removal if it is absent from the new scan. Acquisition source and scan timestamp are excluded from comparison. unmodeled_changes lists same-identity records whose raw-record hash changed while normalized fields match; these are not counted as unchanged. This can indicate a change to fields outside the snapshot model, or array reordering in the raw record. Package and remote ordering alone is not a normalized field change. Entries in changed may also contain changes outside the snapshot model; inspect the raw record to see those changes. Compare reports with the same selection scope: a server absent from the current input is reported as removed, which does not prove it was removed from a registry.

The comparison file must be a local report produced by this registry mode with valid normalized snapshots. Malformed, oversized or duplicate server identities are rejected. This option requires --mcp-registry and is separate from the finding-suppression --baseline: it never suppresses findings or changes risk scores, and it does not fetch or execute listed server endpoints.

Registry mode rejects skill-only controls such as --use-shipped-baseline, --transitive, --transitive-depth, --transitive-allow-prefix, and --transitive-deny-prefix with exit code 2, including an explicitly supplied default depth (--transitive-depth 1). Omit these options for registry scans.

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.

Explicit scan scope

For a local directory containing one SKILL.md or skill.md, repeat --exclude to omit selected files before content analysis:

skillspector scan ./my-skill --exclude 'tests/*' --exclude 'fixtures/*.json'

Patterns are case-sensitive globs matched against the entire relative POSIX path; * also matches /. Quote patterns so your shell does not expand them. This is an explicit caller option, not an author-controlled ignore file. Patterns matching either manifest name (SKILL.md or skill.md) are rejected, as are recursive, multi-skill, transitive, registry, and non-directory inputs. No-match patterns are still recorded in the report.

Files already outside the scan inventory, such as policy-excluded dependencies, retain their existing policy handling and are not counted as caller exclusions. Explicitly excluded inventory files remain in the coverage denominator as entirely uninspected. The report lists the applied patterns, excluded count and individual skipped paths; any matched exclusion makes coverage partial and prevents a SAFE recommendation. Use --fail-on-incomplete when partial scope should fail CI. These exclusions do not suppress findings in included files or override existing safety limits.

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 (meta_analyzer: claude-sonnet-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
nv_build	NVIDIA_INFERENCE_KEY	build.nvidia.com	z-ai/glm-5.3
gemini	GOOGLE_CLOUD_PROJECT (+ optional GOOGLE_CLOUD_LOCATION) via ADC	Google Cloud OpenAI-compatible Gemini endpoint	gemini-3.8-flash
ollama	(none)	OLLAMA_BASE_URL (default http://localhost:11434/v1)	llama3.1:8b
azure_openai	AZURE_OPENAI_API_KEY + AZURE_OPENAI_ENDPOINT	Azure OpenAI Service	gpt-4o (deployment defaults to the model label)
openai_compatible	SKILLSPECTOR_COMPAT_API_KEY + SKILLSPECTOR_COMPAT_BASE_URL	Any OpenAI-compatible endpoint	llama-3.1-70b-versatile
claude_cli	(none — uses local CLI auth)	local claude binary	local Claude runtime fallback, or SKILLSPECTOR_MODEL
codex_cli	Disabled	Registered for compatibility; its read-only sandbox permits host-file reads	Use an HTTP API provider or another supported CLI provider
gemini_cli	(none — uses local CLI auth)	local gemini binary	local Gemini runtime fallback, or SKILLSPECTOR_MODEL
opencode_cli	(none — uses local CLI auth)	local opencode (verified 1.18.31–1.18.35, 1.19.0–1.19.4) binary	local OpenCode runtime fallback, or SKILLSPECTOR_MODEL

For openai_compatible gateways requiring extra HTTP headers, set SKILLSPECTOR_COMPAT_HEADERS='{"X-Tenant-ID":"my-tenant","X-API-Key":"gateway-key"}'. It must be a JSON object with string keys and values and applies to sync and async requests. Unset or blank values add no headers; invalid configuration is rejected without exposing header values. Treat this environment variable as a secret when it contains gateway API keys.

gpt-6.1-sol, claude-opus-5-5, and claude-sonnet-5-5 remain opt-in through SKILLSPECTOR_MODEL or per-slot overrides. Promoting them to defaults requires live scanner acceptance covering benign input, credential disclosure, prompt injection, refusals, and incomplete responses. Record findings, completion status, latency, and usage, and compare against previous defaults where accessible.

For NVIDIA Build's z-ai/glm-5.3, SkillSpector requests high reasoning effort. Set SKILLSPECTOR_REASONING_EFFORT to override it with low, high, or max. The bundled 128,000-token context and 32,000-token output budgets are conservative application limits; they do not claim the hosted endpoint's maximum capacity.

Structured output is requested through LangChain's with_structured_output. The default is client-specific: ChatOpenAI uses json_schema, while other clients may use tool calling. Some models reject a forced tool call with HTTP 400 (tool_choice: type "tool" and "any" are not supported for this model). The anthropic and anthropic_proxy providers route those models (claude-opus-5-5, claude-sonnet-5-5, claude-fable-5-1, claude-mythos-5-1, or any registry entry with structured_output: json_schema) to the native JSON-schema response format. Bedrock has no JSON-schema output for them, so the bedrock provider leaves toolChoice at auto, asks for the tool call in the prompt, and retries a prose answer; it recognises the model from the model ID, a geo/global inference-profile ID, or a foundation-model / inference-profile ARN. An application-inference-profile ARN hides the model, so add that ARN to the registry (SKILLSPECTOR_MODEL_REGISTRY) with tool_choice: auto. The openai_compatible provider honours the same tool_choice: auto entry for endpoints that ignore both response_format and a forced tool_choice and answer in prose (for example iFlytek's spark-x2.5, which is bundled). The anthropic, anthropic_proxy, openai, and openai_compatible providers also recognise those Claude models inside gateway model IDs such as anthropic/claude-opus-5-5 or aws/anthropic/bedrock-claude-opus-5-5. Some OpenAI-compatible gateways turn a json_schema response format for Claude into a forced tool call, so the openai and openai_compatible providers bind the schema as a tool with tool_choice left at auto and retry a prose answer. SKILLSPECTOR_STRUCTURED_OUTPUT_METHOD=json_schema|function_calling overrides the method for any provider. The method precedence is SKILLSPECTOR_STRUCTURED_OUTPUT_METHOD, the provider's method hint, an analyzer preference, then the client default. TP4 prefers function_calling only for provider openai and the exact model label azure/anthropic/claude-opus-5, with reasoning/thinking controls unset; dated or suffixed labels and other providers keep their existing behavior. An explicit environment override or provider hint still wins when reasoning is configured.

# 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
# Override with any Bedrock model ID, cross-region inference-profile
# ID, or your own application-inference-profile ARN. Claude 5.5 is opt-in:
# us./eu. profiles keep requests in that geography, global. routes worldwide.
# export SKILLSPECTOR_MODEL=us.anthropic.claude-sonnet-5-5
skillspector scan ./my-skill/

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

# Gemini on Google Cloud (Application Default Credentials / Workload Identity)
# Prerequisites:
#   1. Google Cloud project with billing enabled.
#   2. Enable Gemini Enterprise Agent Platform / Vertex AI API: `aiplatform.googleapis.com`.
#   3. IAM permission: grant `roles/aiplatform.user` (or at minimum `aiplatform.endpoints.predict`)
#      to your user account or Kubernetes service account.
#   4. Local authentication: run `gcloud auth application-default login`.
#      Configure a quota project if needed: `gcloud auth application-default set-quota-project PROJECT_ID`.
#   5. Kubernetes / GKE: configure Workload Identity and leave GOOGLE_APPLICATION_CREDENTIALS unset
#      rather than exporting service account keys.
# Note on Data Residency:
#   The default `global` endpoint does not support data-residency requirements. While you can target
#   `us`, `eu`, or regional endpoints (e.g. `us-central1`), endpoint selection alone does not guarantee
#   data residency or in-region processing without appropriate organizational policies. Always verify
#   that your selected model is supported in your target location.
# Optional credentials:
#   GOOGLE_APPLICATION_CREDENTIALS is optional and can reference Workload or Workforce Identity Federation
#   configuration files; exporting long-lived service account keys is discouraged.
export SKILLSPECTOR_PROVIDER=gemini
export GOOGLE_CLOUD_PROJECT=my-project-id
# export GOOGLE_CLOUD_LOCATION=global  # default is global; or us, eu, or specific region (e.g. us-central1)
# Default model: gemini-3.8-flash
# export SKILLSPECTOR_MODEL=gemini-3.7-flash
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/

# Gemini (via OpenAI compatibility layer)
export SKILLSPECTOR_PROVIDER=openai
export OPENAI_API_KEY="YOUR_GEMINI_API_KEY"
export OPENAI_BASE_URL="https://generativelanguage.googleapis.com/v1beta/openai/"
export SKILLSPECTOR_MODEL=gemini-3.5-flash
skillspector scan ./my-skill/

# Local Ollama — no API key
export SKILLSPECTOR_PROVIDER=ollama
# export OLLAMA_BASE_URL=http://localhost:11434/v1  # shown default
export SKILLSPECTOR_MODEL=llama3.1:8b
skillspector scan ./my-skill/

# Azure OpenAI
export SKILLSPECTOR_PROVIDER=azure_openai
export AZURE_OPENAI_API_KEY=...
export AZURE_OPENAI_ENDPOINT=https://example.openai.azure.com/
export AZURE_OPENAI_DEPLOYMENT=my-deployment
skillspector scan ./my-skill/

# Any other OpenAI-compatible endpoint
export SKILLSPECTOR_PROVIDER=openai_compatible
export SKILLSPECTOR_COMPAT_API_KEY=...
export SKILLSPECTOR_COMPAT_BASE_URL=https://api.groq.com/openai/v1
export SKILLSPECTOR_MODEL=llama-3.1-70b-versatile
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 local MCP-capable agents (Claude Code, Codex CLI, Gemini CLI) 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 transport on a local loopback interface
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. HTTP bindings are restricted to loopback IPs (127.0.0.1 or ::1); localhost binds to 127.0.0.1 without DNS resolution. Wildcard, routable and other hostname bindings are rejected.

• For remote access, put an authenticating reverse proxy (e.g. nginx + mTLS) in front of the loopback listener.
• 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.

HTTP scans accept only credential-free https:// targets. Git scans never use the server's Git login or Git config. If a proxy or custom CA is needed, set HTTPS_PROXY, NO_PROXY or GIT_SSL_CAINFO in the server environment. Use stdio or the CLI for private repositories. Git redirects are rejected; use the current repository URL after a rename or transfer.

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 (10+ 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; root-level visible source helpers in manifested .agents/skills/<name>/ and .claude/skills/<name>/ folders are exempted but remain local-only and are marked as not LLM-reviewed
SC10	Dependency Source Redirection	HIGH	Package-manager source added, replaced, or unresolved

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

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