@guardbee/mcp-prompt-injection-scanner
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[npm version]
[npm downloads]
[License: MIT]
An MCP (Model Context Protocol) server that scans content — a RAG chunk, a scraped web page, a document — for indirect prompt injection payloads.
ai-code-scanner and mcp-server-auditor scan code; this package scans data. In classic prompt injection, the attacker writes a malicious prompt themselves; in indirect prompt injection, the attacker never talks to the model at all — instead they embed instructions in a document, web page, or tool result the model will later read. The moment the model pulls this content into its context via a RAG retrieval or a web fetch, the embedded instruction appears to carry the same authority as the user's own instructions.
Telemetry is off by default. With GUARDBEE_TELEMETRY=1 it sends the server, tool, success, duration and short parameter values such as a file path; longer strings and fields like content, token or password are replaced with a placeholder. See @guardbee/mcp-telemetry.
Features
- 17 patterns, 5 categories — instruction-override, role-spoofing, hidden-text, direct-address, exfiltration. English and Turkish phrasing
- The rules live in
@guardbee/guard-core, shared with @guardbee/mcp-security-proxy, so a phrase this scanner reports is the same phrase the proxy blocks at runtime
- Every finding includes why it's risky and what to do about it (
recommendation) — not just "found it"
- SARIF 2.1.0 output — CI/CD integration (e.g. auto-scanning a knowledge-base repo on every PR)
- Config file support via
guardbee.yml
- 45 unit tests — a positive and a negative (false-positive) scenario for every pattern; known false-positive sources like emoji ZWJ sequences and ordinary
display:none modals are specifically tested
Quick Start
Claude Desktop / MCP Client
CLI (CI/CD — e.g. scan a RAG knowledge-base repo on every PR)
MCP Tools
Detected Patterns
These are heuristic findings — a static text-pattern scan, not a semantic/intent analysis. Designed for a low false-positive rate (e.g. emoji ZWJ sequences and ordinary display:none modals are specifically excluded), but every finding should still be reviewed manually.
LLM judge (optional)
The patterns know attack phrases; a paraphrased, indirect or translated instruction slips past them. --judge also has a model read each file, in chunks, and reports instructions aimed at an agent as judge_semantic_injection (high). The finding carries judge: { model, version, confidence }, also in SARIF properties. The judge only adds findings.
It is off unless configured. Since the content goes to the endpoint, a non-local one needs --judge-allow-remote and https. On GuardBee's judge benchmark (50 English and Turkish samples), the patterns catch 16% of the attacks and qwen2.5:7b all of them, with no false positives.
Configuration (guardbee.yml)
Development
License
MIT — GuardBee
Feedback
Questions, ideas, or a false positive? Start a thread in GitHub Discussions or open an issue. If GuardBee is useful to you, a ⭐ on the repository helps others find it.