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Bumps [actions/checkout](https://github.com/actions/checkout) from 2 to 3. - [Release notes](https://github.com/actions/checkout/releases) - [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md) - [Commits](actions/checkout@v2...v3) --- updated-dependencies: - dependency-name: actions/checkout dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com>
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OK, I won't notify you again about this release, but will get in touch when a new version is available. If you'd rather skip all updates until the next major or minor version, let me know by commenting If you change your mind, just re-open this PR and I'll resolve any conflicts on it. |
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- Add tag-and-release workflow for automatic tagging and PyPI publishing - Add create-release workflow for manual release creation with version bump PR
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Implements a comprehensive scanner to detect indirect prompt injection vulnerabilities in LLM-based systems, addressing OWASP LLM Top 10 Giskard-AI#1 risk. Features: - 111 attack patterns across 4 categories: * RAG Context Poisoning (38 patterns): HTML comments, YAML, JSON, encoding * Tool Manipulation (24 patterns): Function hijacking, API injection * External Content Injection (33 patterns): Websites, emails, PDFs, metadata * Multi-Source Context Mixing (16 patterns): Priority override attacks - Complete implementation following Giskard architecture patterns: * IndirectInjectionDataLoader with local CSV fallback * LLMIndirectInjectionDetector with @detector registration * StringMatcherEvaluator integration for detection * Comprehensive unit tests - IssueGroup definitions added for all 3 planned scanners: * IndirectInjection (for this scanner) * MultiTurnJailbreak (for future scanner 2) * TrainingDataLeakage (for future scanner 3) Testing: - All 7 integration tests passing - Correctly detects vulnerable models (50-71% attack success rates) - Zero false positives on safe models - Proper severity classification (MAJOR/MEDIUM/MINOR) This scanner enables security testing for RAG systems, tool-using agents, and document processors against real-world indirect injection attacks. References: - OWASP LLM Top 10 2025: LLM01 Prompt Injection - Lakera: Indirect Prompt Injection Threats - OpenRAG-Soc benchmark research
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Bumps actions/checkout from 2 to 3.
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a12a394update readme for v3 (#708)8f9e05eUpdate to node 16 (#689)230611dChange secret name for PAT to not start with GITHUB_ (#623)Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
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