{"data":{"slug":"neerazz-embedguard","name":"embedguard","tagline":"Cross-Layer Detection and Provenance Attestation for Adversarial Embedding Attacks in RAG Systems","github_url":"https://github.com/neerazz/embedguard","owner":"neerazz","repo":"embedguard","owner_avatar_url":"https://avatars.githubusercontent.com/u/43318996?v=4","primary_language":"Python","stars":0,"forks":0,"topics":["ai-safety","embedding-attacks","llm","llm-security","prompt-injection","provenance","rag","rag-security","trusted-execution-environment","vector-database"],"archived":false,"github_pushed_at":"2026-08-18T20:09:05+00:00","maintenance_label":"Steady","stars_delta_30d":0,"url":"https://www.graphcanon.com/tools/neerazz-embedguard","markdown_url":"https://www.graphcanon.com/tools/neerazz-embedguard.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/neerazz-embedguard","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=neerazz-embedguard","description":"Cross-Layer Detection and Provenance Attestation for Adversarial Embedding Attacks in RAG Systems","homepage_url":null,"license":"MIT","open_issues":0,"watchers":0,"ai_summary":"A tool for detecting and attesting provenance against adversarial embedding attacks in RAG systems, focusing on AI safety and security.","readme_excerpt":"### Quick start\n\n```bash\ngit clone https://github.com/neerazz/embedguard\ncd embedguard\npip install -e .\n\n---\n\n# Install in development mode\npip install -e \".[dev]\"\n```\n\n---\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.","github_created_at":"2026-01-24T21:35:51+00:00","created_at":"2026-07-11T23:06:27.140953+00:00","updated_at":"2026-09-20T04:29:00.855345+00:00","categories":[{"slug":"evaluation-observability","name":"Evaluation & Observability","url":"https://www.graphcanon.com/categories/evaluation-observability","markdown_url":"https://www.graphcanon.com/categories/evaluation-observability.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/evaluation-observability"},{"slug":"llm-frameworks","name":"LLM Frameworks","url":"https://www.graphcanon.com/categories/llm-frameworks","markdown_url":"https://www.graphcanon.com/categories/llm-frameworks.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/llm-frameworks"},{"slug":"vector-databases","name":"Vector Databases","url":"https://www.graphcanon.com/categories/vector-databases","markdown_url":"https://www.graphcanon.com/categories/vector-databases.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/vector-databases"}],"tags":[{"slug":"ai-safety","name":"ai-safety"},{"slug":"embedding-attacks","name":"embedding-attacks"},{"slug":"llm","name":"llm"},{"slug":"llm-security","name":"llm-security"},{"slug":"prompt-injection","name":"prompt-injection"},{"slug":"provenance","name":"provenance"},{"slug":"rag","name":"rag"},{"slug":"rag-security","name":"rag-security"}],"trust":{"provenance":{"is_fork":false,"github_id":1141450053,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-18T08:12:08.576Z","maintenance":{"label":"Steady","score":60,"methodology":"github_public_v1","releases_90d":2,"days_since_push":30,"last_release_at":"2026-07-09T12:13:25Z","stars_delta_30d":0,"open_issues_delta_30d":0},"security_summary":{"status":"findings","scanner":"osv@v1","low_count":4,"high_count":0,"last_scan_at":"2026-09-18T08:12:11.077Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-18T08:12:06.877Z"},"deploy":{"source":"dockerfile:Dockerfile","self_host":true,"observed_at":"2026-09-18T08:12:06.877Z","managed_saas":false},"has_cli":{"value":true,"source":"pyproject.toml:[project.scripts]","observed_at":"2026-09-18T08:12:06.877Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-09-18T08:12:06.877Z"},"has_docker":{"value":true,"source":"dockerfile:Dockerfile","observed_at":"2026-09-18T08:12:06.877Z"},"license_spdx":{"value":"MIT","source":"github.license","observed_at":"2026-09-18T08:12:06.877Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":{"notes":["Installation requires Python and pip.","Development mode installation is supported."],"min_ram_gb":null,"requires_docker":false},"constraints":{"min_ram_gb":null,"requires_docker":false},"when_to_use":["When you need to ensure the integrity of embeddings in RAG systems against adversarial attacks.","If your project involves AI safety and security, particularly in the context of prompt injection and provenance verification."],"when_not_to_use":["If your focus is on general-purpose AI model evaluation without a specific need for adversarial embedding attack detection.","When your application does not involve RAG systems or you are not concerned with embedding security and provenance."],"source":"enrich:decision_facts","observed_at":"2026-09-18T09:20:28.037Z"},"constraint_facets":{"min_ram_gb":null,"requires_docker":false},"decision_summary":[{"label":"Requirements","value":"Installation requires Python and pip.; Development mode installation is supported."},{"label":"Adopt for","value":"Embedguard is a Python tool designed for detecting and attesting provenance against adversarial embedding attacks in RAG systems, emphasizing AI safety and security."},{"label":"License detail","value":"MIT License"}]}}