prompt-injection-mcp
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- AlicenseBqualityDmaintenanceEnables testing AI safety classifier robustness against query decomposition, obfuscation, and multi-agent attacks. Provides tools for full evaluation pipelines, query previews, and status checks.46MIT
- AlicenseBqualityCmaintenanceProvides local, dependency-free security scanning tools for LLM configurations, prompts, RAG sources, and more, enabling AI coding agents to detect prompt injections and other vulnerabilities without external network access.8MIT
- AlicenseNot gradedqualityAmaintenanceEnables users to scan untrusted text, files, and directory trees for prompt-injection patterns and receive severity-ranked, explainable verdicts before content reaches an agent. It runs locally and returns clean, review, or blocked verdicts for safer agent input.MIT
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- AlicenseAqualityCmaintenanceEnables LLM application builders and security teams to detect and strip prompt injections, scan for secrets, obfuscate PII, and audit synthetic datasets for bias using deterministic tools.5MIT
TDQS
Scored across 8 tools
Each tool targets a distinct phase of the testing workflow: listing categories, retrieving payloads (by category, ID, or keyword), generating custom payloads, running tests, analyzing responses, and reporting. Minor overlap exists between search_payloads and get_payloads_by_category, but their scopes and verbs are clearly differentiated.
All tool names use consistent snake_case and follow a verb_noun or verb_noun_preposition pattern (list_, get_, run_, generate_, analyze_, search_). No conventions are mixed.
8 tools cover the entire prompt-injection testing lifecycle without redundancy. Each tool maps to a natural workflow step, making the set lean yet sufficient.
The core workflow from payload discovery to report generation is fully covered. Missing management operations for payloads (update/delete) and test sequences, but the read/generate/run/analyze path is complete for typical testing use.