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Alternatives to prompt-injection-mcp

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    • A
      license
      B
      quality
      D
      maintenance
      Enables testing AI safety classifier robustness against query decomposition, obfuscation, and multi-agent attacks. Provides tools for full evaluation pipelines, query previews, and status checks.
      4
      5
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    • A
      license
      B
      quality
      C
      maintenance
      Provides 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.
      8
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    • A
      license
      B
      quality
      D
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      Enables security teams to run controlled adversarial penetration tests against authorized ML/LLM API endpoints, scoring responses and generating evidence for compliance frameworks such as SOC 2, ISO 27001, and GDPR.
      6
      2
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    • A
      license
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      quality
      B
      maintenance
      Provides deterministic scanning and detection of prompt injection, jailbreak, data-exfiltration, and system-prompt leaks, with EIP-191 signed attestations and 0G Storage anchoring for verifiable safety reports.
      1
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    • F
      license
      A
      quality
      D
      maintenance
      Enables AI assistants to register prompts for security verification, checking for hidden injections, data exfiltration patterns, and jailbreak attempts, then generate embed codes for displaying security badges on websites.
      4
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    • A
      license
      Not graded
      quality
      B
      maintenance
      Analyzes inputs and outputs in real-time to protect against prompt injections, data leaks, secrets exposure, and phishing URLs.
      7 npm
      3
      MIT

    TDQS

    B3.2/5.0

    Scored across 8 tools

    Disambiguation4/5

    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.

    Naming Consistency5/5

    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.

    Tool Count5/5

    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.

    Completeness4/5

    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.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues