--- title: "Open-Prompt-Injection vs embedguard" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/liu00222-open-prompt-injection-vs-neerazz-embedguard" tools: ["liu00222-open-prompt-injection", "neerazz-embedguard"] --- # Open-Prompt-Injection vs embedguard *GraphCanon updated Oct 4, 2026* ## Verdict Pick Open-Prompt-Injection if open-Prompt-Injection is a Python-based toolkit for benchmarking prompt injection attacks on LLMs, offering customization through config files and support for various LLM APIs; pick embedguard if embedguard is a Python tool designed for detecting and attesting provenance against adversarial embedding attacks in RAG systems, emphasizing AI safety and security. [Open-Prompt-Injection](https://github.com/liu00222/Open-Prompt-Injection) reports 503 GitHub stars, 81 forks, and 2 open issues, last pushed Sep 27, 2026. [embedguard](https://github.com/neerazz/embedguard) has 0 stars, 0 forks, and 0 open issues, last pushed Aug 18, 2026. Figures are from public GitHub metadata via [Open-Prompt-Injection's repository](https://github.com/liu00222/Open-Prompt-Injection) and [embedguard's repository](https://github.com/neerazz/embedguard). | | [Open-Prompt-Injection](/tools/liu00222-open-prompt-injection.md) | [embedguard](/tools/neerazz-embedguard.md) | | --- | --- | --- | | Tagline | Benchmark and toolkit for prompt injection attacks and defenses in LLMs | Cross-Layer Detection and Provenance Attestation for Adversarial Embedding Attacks in RAG Systems | | Stars | 503 | 0 | | Forks | 81 | 0 | | Open issues | 2 | 0 | | Language | Python | Python | | Adopt for | Open-Prompt-Injection is a Python-based toolkit for benchmarking prompt injection attacks on LLMs, offering customization through config files and support for various LLM APIs. | Embedguard is a Python tool designed for detecting and attesting provenance against adversarial embedding attacks in RAG systems, emphasizing AI safety and security. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT License | | Categories | Evaluation & Observability, LLM Frameworks | Evaluation & Observability, LLM Frameworks, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Open-Prompt-Injection](/tools/liu00222-open-prompt-injection.md) | [embedguard](/tools/neerazz-embedguard.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 6d | 30d | | Open issues (now) | 2 | 0 | | Stars delta | +33 (30d) | 0 (30d) | | Open issues delta | -12 (30d) | 0 (30d) | | Full report | [trust report](/tools/liu00222-open-prompt-injection/trust.md) | [trust report](/tools/neerazz-embedguard/trust.md) | ## Shared compatibility - **Python**: [Open-Prompt-Injection](/tools/liu00222-open-prompt-injection.md) - Python runtime; [embedguard](/tools/neerazz-embedguard.md) - Python runtime ## Decision facts: Open-Prompt-Injection - **Adopt for:** Open-Prompt-Injection is a Python-based toolkit for benchmarking prompt injection attacks on LLMs, offering customization through config files and support for various LLM APIs. ## Decision facts: embedguard - **Requirements:** Installation requires Python and pip.; Development mode installation is supported. - **Adopt for:** Embedguard is a Python tool designed for detecting and attesting provenance against adversarial embedding attacks in RAG systems, emphasizing AI safety and security. - **License detail:** MIT License ## Choose when ### Choose Open-Prompt-Injection if… - Tags unique to Open-Prompt-Injection: security-and-privacy. - You prioritize security testing specifically for prompt injection vulnerabilities in your LLM applications. - More GitHub stars (503 vs 0) - visibility, not fit. ### Choose embedguard if… - Requirements: Installation requires Python and pip.; Development mode installation is supported.. - Tags unique to embedguard: ai-safety, embedding-attacks, provenance, rag. - Also covers Vector Databases. - embedguard ships Docker support for self-hosted deployment. - When you need to ensure the integrity of embeddings in RAG systems against adversarial attacks. ## When NOT to use Open-Prompt-Injection - You require broader, more generalized security features not centered on prompt injection attacks. - Your project does not involve working with Google PaLM2 or other specific models like Meta's Llama and OpenAI's GPT. ## When NOT to use embedguard - 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. ## Common questions ### What is the difference between Open-Prompt-Injection and embedguard? Open-Prompt-Injection: Benchmark and toolkit for prompt injection attacks and defenses in LLMs. embedguard: Cross-Layer Detection and Provenance Attestation for Adversarial Embedding Attacks in RAG Systems. See the comparison table for live GitHub stats and shared categories. ### When should I choose Open-Prompt-Injection over embedguard? Choose Open-Prompt-Injection over embedguard when Tags unique to Open-Prompt-Injection: security-and-privacy; You prioritize security testing specifically for prompt injection vulnerabilities in your LLM applications; More GitHub stars (503 vs 0) - visibility, not fit. ### When should I choose embedguard over Open-Prompt-Injection? Choose embedguard over Open-Prompt-Injection when Requirements: Installation requires Python and pip.; Development mode installation is supported.; Tags unique to embedguard: ai-safety, embedding-attacks, provenance, rag; Also covers Vector Databases; embedguard ships Docker support for self-hosted deployment; When you need to ensure the integrity of embeddings in RAG systems against adversarial attacks. ### When should I avoid Open-Prompt-Injection? You require broader, more generalized security features not centered on prompt injection attacks. Your project does not involve working with Google PaLM2 or other specific models like Meta's Llama and OpenAI's GPT. ### When should I avoid embedguard? 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. ### Is Open-Prompt-Injection or embedguard more popular on GitHub? Open-Prompt-Injection has more GitHub stars (503 vs 0). Stars measure visibility, not whether either tool fits your constraints. ### Are Open-Prompt-Injection and embedguard open source? Yes - both are open-source projects on GitHub (Open-Prompt-Injection: MIT, embedguard: MIT). ### Where can I find alternatives to Open-Prompt-Injection or embedguard? GraphCanon lists graph-backed alternatives at [Open-Prompt-Injection alternatives](/tools/liu00222-open-prompt-injection/alternatives) and [embedguard alternatives](/tools/neerazz-embedguard/alternatives) ([Open-Prompt-Injection markdown twin](/tools/liu00222-open-prompt-injection/alternatives.md), [embedguard markdown twin](/tools/neerazz-embedguard/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/liu00222-open-prompt-injection-vs-neerazz-embedguard.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Open-Prompt-Injection or embedguard? Open-Prompt-Injection: Very active. embedguard: Steady. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for Open-Prompt-Injection and embedguard? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Open-Prompt-Injection trust report](/tools/liu00222-open-prompt-injection/trust); [embedguard trust report](/tools/neerazz-embedguard/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=liu00222-open-prompt-injection`](/api/graphcanon/graph?tool=liu00222-open-prompt-injection) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_