List every A2A (Agent2Agent) protocol pattern this auditor detects, grouped by category

Recursively scan a directory of A2A agent source for protocol anti-patterns

Scan a single file for A2A (Agent2Agent) protocol anti-patterns

Scan Agent2Agent (A2A) protocol implementation code for anti-patterns found in the reference SDKs and sample agents: an Agent Card fetching a client-supplied push-notification webhook URL with no host allowlist (SSRF), a request handler wired up with no authentication at all, and a literal credential embedded directly in Agent Card metadata (which is served publicly).

List every dangerous-capability rule this auditor looks for at the end of a delegation path, grouped by category

Recursively scan a directory of Python files and report excessive-agency paths found in each. Each file's agent/tool graph is analyzed independently — variable references don't resolve across files, so a Crew/GroupChat whose members are defined in a different file than the assembling call won't be connected.

Scan a single Python file for excessive-agency paths across LangGraph/CrewAI/AutoGen orchestration constructs

Scan a Python snippet defining a multi-agent orchestration (LangGraph, CrewAI, or AutoGen/ag2) for excessive agency reachable through delegation or group membership — an agent with no dangerous tool of its own that can still reach one (shell/code-exec/file-write/network/credentials) through another agent it can hand off to.

List all AI/LLM security patterns that the scanner can detect, grouped by category (including any custom rules loaded from .guardbee/rules)

Recursively scan a directory for insecure AI/LLM integration patterns. Automatically skips node_modules, .git, dist, and binary files.

Scan a single file for insecure AI/LLM integration patterns

Scan a text string or code snippet for insecure AI/LLM integration patterns (client-exposed keys, unsafe output handling, excessive agent tool permissions, PII sent to a model, prompt injection surface).

List every OWASP MCP08 audit-gap check this auditor runs

Recursively scan a directory of MCP server source for OWASP MCP08 audit/telemetry gaps

Scan a single file for OWASP MCP08 audit/telemetry gaps

Scan MCP server source for OWASP MCP08:2025 (Lack of Audit and Telemetry) gaps: tools registered with no audit trail, raw args/results logged, audit hard-disabled, silent catch blocks, audit events without a session/request id.

Fetch and analyze a privacy policy page for completeness against GDPR/KVKK requirements. Works without an API key.

Run a full compliance scan for a URL against a regulatory framework (KVKK, GDPR, or CCPA) using the GuardBee scanner. Returns a gap analysis and action plan.

Compare KVKK, GDPR, and CCPA requirements side by side to understand overlaps and gaps

Get compliance findings from a completed GuardBee scan, optionally filtered by severity

List all compliance requirements for a framework with legal basis references

List every OWASP MCP10 context-oversharing check this auditor runs

Recursively scan a directory for OWASP MCP10 context-oversharing issues

Scan a single file for OWASP MCP10 context-oversharing issues

Scan agent/MCP source for OWASP MCP10:2025 context oversharing — full conversation dumps, unscoped memory tools, shared global session stores, system-prompt leaks via tools, cross-session tool-result bleed, and unfiltered vector queries.

Get the column names and gateway masking policy for a table — helps the LLM understand what data it can access.

Show the active gateway configuration: field masking rules, table policies, and audit settings.

List all available database tables. Tables marked as 'deny' in gateway policy are omitted.

Query the gateway's own audit log — inspect which tools were called, on which tables, whether they were allowed or denied (and why), and which fields were redacted/written. Covers events since this server process started; count is capped by the gateway's audit.bufferSize (default 200).

Query rows from a database table with optional filters. All results pass through the KVKK/GDPR gateway — sensitive fields are automatically masked.

Auto-detect and audit every supported project in a directory: npm (package-lock, pnpm, yarn, bun), Python (uv, Poetry, Pipenv, requirements), Rust (Cargo.lock), Go (go.mod), Ruby (Gemfile.lock) and PHP (composer.lock)

Audit npm dependencies for known CVEs using the OSV database. Reads exact versions from package-lock.json, npm-shrinkwrap.json, pnpm-lock.yaml, yarn.lock (v1 and berry) or bun.lock, falling back to package.json ranges

Audit a single package by name, version, and ecosystem

Audit Python dependencies for known CVEs using the OSV database. Reads exact versions from uv.lock, poetry.lock or Pipfile.lock, falling back to requirements.txt / pyproject.toml

Audit several domains at once (e.g. every brand or country domain) and compare them: an A–F grade per domain from all DNS, email, DNSSEC, CAA and nameserver checks, plus each domain's most serious findings

Check a domain's email security: MX, SPF (nested lookup count), DMARC, DKIM key strength, MTA-STS and TLS-RPT, with remediation advice

Check specific hostnames (e.g. from your DNS zone or certificate logs) for subdomain takeover

Enumerate all DNS records for a domain and detect misconfigurations: SPF, DMARC, DKIM key strength, MTA-STS/TLS-RPT, MX, DNSSEC (signed / broken / weak algorithms) and CAA (including whether it blocks the CA of the live certificate) and nameservers (lame delegation, anonymous zone transfer, open recursion)

Find subdomains from Certificate Transparency logs and a common-name wordlist, and detect subdomain takeovers: CNAMEs to S3, GitHub Pages, Azure, Heroku, Netlify and other providers whose resource is gone (verified fingerprints), or to targets that no longer exist

Perform a targeted DNS lookup for specific record types on a domain

List the PII and credential detectors this scanner runs

Scan a document given as base64 (an attachment, an image a tool returned) for PII and credentials. The type is detected from the bytes.

Find PII (TC Kimlik No, IBAN, card, passport, e-mail, phone, address…) and credentials inside a PDF, an image (screenshot, scanned page) or a text file on disk, before it is given to an LLM. PDFs are read from their text layer; images and scanned pages with OCR (Turkish and English).

List every elicitation check this auditor runs

Recursively scan a directory of MCP server source for elicitation anti-patterns

Scan a single file for elicitation anti-patterns

Scan MCP server source for elicitation anti-patterns from the 2026-07-28 spec: form mode requesting secrets or embedding a link, URL mode opened directly on a third-party authorize endpoint, credentials in the elicitation URL, a form answer used as the user's identity, and a result used without checking decline or cancel.

List all red-team probes this tool can run against a target LLM/chatbot, grouped by category. Every probe is canary-based (safe) — none ask the target to produce genuinely harmful content.

Run canary-based red-team probes against a live LLM endpoint or chatbot you control, to test whether known jailbreak/extraction/obfuscation techniques bypass its guardrails. ONLY point this at an endpoint you own or are explicitly authorized to test — every probe consumes API quota on the target. Probes never request genuinely harmful content; success is measured by whether the target reproduces a random one-time token, proving an override instruction was obeyed.

OWASP MCP09:2025 — discover MCP client configs in well-known host paths (Cursor, Claude Desktop, Windsurf, VS Code, Claude Code) and flag servers that are not on an organization allowlist (Shadow MCP). Does not start any MCP server.

List the well-known MCP client config paths this auditor checks during shadow (MCP09) discovery on the current OS.

List the MCP config, agent skill, shadow-MCP discovery, and cross-server shadowing checks, with their OWASP MCP Top 10 tags.

Scan an MCP client config JSON string (Cursor mcp.json, Claude Desktop, Windsurf, or VS Code) for unpinned packages, secrets, wildcard auto-approve, unauthenticated remote endpoints, and typosquats.

Find mcp.json, mcp_config.json, claude_desktop_config.json, and SKILL.md under a directory and audit each one. A directory scan also compares skill names. Does not start any MCP server.

Scan one MCP client config file, or a SKILL.md agent skill when the file has that name.

Detect cross-server tool shadowing from an inventory of tool catalogs. Pass the tool names and descriptions already collected from each installed server. This tool does not connect to those servers.

Scan one agent SKILL.md (YAML frontmatter plus body) for unrestricted allowed-tools, instruction override, credential file reads, and literal secrets. Name collisions across skills require a directory scan.

List all MCP server security patterns that the auditor can detect, grouped by category

Recursively scan an MCP server's codebase for insecure tool-definition patterns. Automatically skips node_modules, .git, dist, and binary files.

Scan a single file for insecure MCP server/tool-definition patterns

Scan a text string or code snippet for insecure MCP server/tool-definition patterns (excessive-agency tool names, shell/eval/SQL/SSRF sinks fed by raw tool input, overly permissive schemas, hardcoded secrets, wildcard CORS).

List every memory-write and memory-readback pattern this scanner detects, grouped by category

Recursively scan a directory of agent source code for memory-poisoning patterns

Scan a single file for memory-write and memory-readback poisoning patterns

Scan agent code for memory-poisoning risks: untrusted input written into persistent, cross-session memory (MemGPT-style archival/core memory, or a vector store used as a long-term knowledge base) without visible sanitization, and memory retrievals fed into a system/assistant-role message or prompt template as trusted context.

List every dangerous Python global (module.function) this scanner treats as a red flag when found referenced inside a pickled model file, grouped by risk category

Recursively scan a directory for ML model files (.pt, .pth, .ckpt, .pkl, .pickle, .bin, .dill, .joblib, .jbl, .npy, .npz, .safetensors, .h5, .hdf5, .keras, .onnx, .gguf, .pb, .pbtxt, .tflite; a TensorFlow SavedModel directory through its saved_model.pb) and report supply-chain risks in each. Automatically skips node_modules, .git, venv, and other non-model directories.

Scan a single ML model file for supply-chain risks: dangerous Python globals reachable during pickle deserialization (os/subprocess/eval/socket/dill gadgets, inside PyTorch zip checkpoints, joblib dumps and NumPy object arrays too), a malformed or disguised .safetensors header, a Keras Lambda-layer RCE pattern in .h5/.keras files, a path-traversal external_data reference in .onnx files, malformed GGUF headers and chat templates that escape the Jinja sandbox or carry hidden instructions, and TensorFlow SavedModel/GraphDef/TFLite graphs that read or write files or call Python.

Scan every model file in a Hugging Face model repo at a pinned commit. The revision (branch, tag or commit sha, default main) is resolved to a commit sha and all files are read from it. Files over 256 MB are checked from their first 64 MB (and last 64 MB for zip containers), which hold the headers and pickle streams the checks read. Gated or private repos need HF_TOKEN in the server's environment.

List every OAuth/token-handling pattern this auditor detects, grouped by category

Recursively scan a directory of MCP server source for OAuth/token-handling anti-patterns

Scan a single file for OAuth/token-handling anti-patterns

Scan an MCP server's authorization code for OAuth 2.1 anti-patterns named in the MCP spec's own Security Considerations: token passthrough (forwarding a client's token to a downstream API unchanged), missing audience validation on JWT verification, OAuth/OIDC discovery SSRF, missing PKCE, loose redirect_uri validation, and hardcoded client secrets.

List OWASP MCP Top 10 categories and which GuardBee scanners cover them in this meta-scan

Audit a tools/list JSON dump for toxic flows and tool-description poisoning (MCP03/MCP10).

Connect to a live MCP server (stdio or HTTP), call tools/list, then grade toxic-flow + tool-poisoning (MCP03/MCP10).

Run a unified OWASP MCP Top 10 scan over a filesystem path — orchestrates secret, server, oauth, audit-gap, context-oversharing, toxic-flow, and tool-poisoning auditors; optional MCP04 supply-chain and MCP06 content injection. Returns A–F grade.

List all indirect prompt injection patterns that the scanner can detect, grouped by category

Recursively scan a directory — e.g. a RAG knowledge base or a corpus of scraped pages — for indirect prompt injection payloads. Automatically skips node_modules, .git, dist, and binary files.

Scan a single file (a knowledge-base document, an HTML page, extracted text) for indirect prompt injection payloads

Scan a text string — a RAG chunk, a scraped web page, a document, a tool result — for indirect prompt injection payloads (instruction-override phrases, spoofed chat-template roles, hidden/invisible text, content addressing 'the AI' directly, data-exfiltration attempts) before it reaches a model's context.

List every credential/PII pattern this scanner detects, grouped by category

Recursively scan a directory of prompt logs or request-body fixtures for leaked credentials and PII

Scan a single file — plain text, or a JSON chat-request-body fixture (auto-detected) — for leaked credentials and PII

Scan an OpenAI/Anthropic-style chat request body (a JSON object with a messages array and/or a system field) for leaked credentials and PII in every message's text content, before it would be sent to the model.

Scan a raw text string (e.g. a prompt you're about to send to an LLM) for leaked credentials (API keys, private keys, JWTs) and PII (TC Kimlik No, credit card, IBAN, email, phone) — checksum-validated where applicable to keep false positives low.

Connect to an MCP server (stdio command or Streamable HTTP URL), read its current tools/list response, and store it as the trusted baseline for future drift checks. Call this once, right after you've reviewed and approved a server's tools.

Connect to an MCP server and compare its current tools/list response against the stored baseline. A tool whose description, schema, or annotations changed since baseline is a 'rug pull' — approved once, silently different now. If no baseline exists yet, one is captured automatically (set autoBaseline=false to require an explicit baseline_server call first).

List every server this scanner has a stored baseline for

List all secret patterns that the scanner can detect

Scan the transcripts coding agents keep on this machine (Claude Code, Codex, Gemini CLI, Continue) for secrets that were pasted into a chat or printed by a command. Such a secret was already sent to the model provider and should be rotated. Each secret is reported once with how often it appears.

Recursively scan a directory for exposed secrets and API keys. Automatically skips node_modules, .git, dist, and binary files.

Scan a single file for exposed secrets and API keys

Scan a git repository for secrets in what git says was added: the staged changes (before a commit) or every commit's added lines (history). History finds secrets that were deleted from the files but are still in the repository.

Scan a text string or code snippet for exposed secrets and API keys

Check a single package name against the real npm or PyPI registry

Auto-detect and check both npm and pip manifests (package.json, requirements.txt, pyproject.toml) in a directory against their real registries

Check every dependency declared in a package.json (dependencies, devDependencies, peerDependencies, optionalDependencies) against the real npm registry -- flags any package name that doesn't exist at all (a likely LLM-hallucinated dependency an attacker may have pre-registered, i.e. 'slopsquatting'), and separately notes any that exist but were only first published very recently.

Check every dependency declared in requirements.txt or pyproject.toml against the real PyPI registry -- flags any package name that doesn't exist at all (a likely LLM-hallucinated dependency an attacker may have pre-registered), and separately notes any that exist but were only first published very recently.

Check certificate expiry for one or more domains and highlight certificates expiring soon

Get detailed certificate information for a domain including fingerprint, SANs, and issuer chain

Inspect TLS certificate, cipher suite, and protocol configuration for a single domain

Inspect TLS configuration for multiple domains in parallel

Evaluate a structured ATR AgentEvent JSON (type, content, fields, sessionId, …).

Read a local file and evaluate its contents against ATR + GuardBee rules

Evaluate free text against Agent Threat Rules (ATR) plus GuardBee KVKK/TR rules. Returns matches with follow-up scanner hints.

Explain how Agent Threat Rules (ATR) complements GuardBee static MCP auditors

Show how GuardBee's runtime findings map to OWASP Top 10 for LLM Applications (2025) and MITRE ATLAS — the mapping guardbee-proxy report uses

List loaded ATR + GuardBee detection rules (optionally filter by category or source).

Summarize loaded rule counts by category and source (ATR vs GuardBee)

List every description-injection phrase and confused-deputy sink rule this scanner detects

Recursively scan a directory of MCP server source for tool-poisoning and confused-deputy patterns

Scan a single file for tool-poisoning and confused-deputy patterns in MCP tool registrations

Scan MCP server source (TypeScript/JavaScript, Python, Go) for tool poisoning (hidden instructions embedded in a tool's description string, read by the calling LLM as trusted context) and confused-deputy tools (a name/description promising read-only behavior while the handler has a shell/eval/file-write/env-dump sink).

Audit a tools/list-shaped JSON catalog for the lethal trifecta (toxic flow): untrusted content + sensitive/private data + exfiltration or destruction on the same MCP server. Returns an A–F grade.

Explain the lethal trifecta / toxic-flow model this auditor uses

Recursively scan a directory of MCP server source for toxic flows across all registered tools

Scan a single source file for toxic flows

Scan TypeScript/JavaScript MCP server source for .registerTool(...) and .tool(...) registrations and audit them for toxic flows

List every Unbounded Consumption / denial-of-wallet pattern this auditor detects, grouped by category

Recursively scan a directory of LLM/agent application source for Unbounded Consumption / denial-of-wallet anti-patterns

Scan a single file for Unbounded Consumption / denial-of-wallet anti-patterns

Scan LLM/agent application code for Unbounded Consumption ("denial of wallet") anti-patterns from OWASP LLM Top 10 2026's #6 category: LLM calls with no output token limit, hand-rolled HTTP calls to an LLM endpoint with no timeout, agent tool-calling or retry loops with no iteration cap, framework safety limits (LangChain max_iterations, openai-agents max_turns) explicitly disabled, and MCP tool handlers that make billable calls with no visible rate limiting.

List the vector-store types this scanner can fingerprint, and what unauthenticated-access checks it runs for each

Probe a vector-database (or vector-index-capable) endpoint for unauthenticated exposure — Weaviate, Qdrant, Chroma, Elasticsearch/OpenSearch (HTTP fingerprinting, escalating from instance info → collection/schema listing → actual stored data), Redis (RESP PING without AUTH), and Postgres/pgvector (wire-protocol SSLRequest + StartupMessage handshake, no real credentials sent). Read-only — never writes or authenticates with real credentials.

Get vulnerability findings from a GuardBee scan with optional severity filter. Includes title, description, and remediation recommendation per finding.

Get the current status and summary of a GuardBee scan by ID

List recent GuardBee scans in your workspace

Run a full GuardBee scan and immediately return all critical and high findings with remediation guidance. Best for quick security assessments.

Start a GuardBee security scan on a URL. Returns a scan ID you can use to check status and get findings.