--- title: "Open-Prompt-Injection vs llm-attacks" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/liu00222-open-prompt-injection-vs-llm-attacks-llm-attacks" tools: ["liu00222-open-prompt-injection", "llm-attacks-llm-attacks"] --- # Open-Prompt-Injection vs llm-attacks *GraphCanon updated Aug 5, 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 llm-attacks if llm-attacks: Universal and Transferable Attacks on Aligned Language Models with dependency on FastChat. [Open-Prompt-Injection](https://github.com/liu00222/Open-Prompt-Injection) reports 470 GitHub stars, 74 forks, and 14 open issues, last pushed Oct 29, 2025. [llm-attacks](https://llm-attacks.org/) has 4.8k stars, 633 forks, and 69 open issues, last pushed Aug 2, 2024. Figures are from public GitHub metadata via [Open-Prompt-Injection's repository](https://github.com/liu00222/Open-Prompt-Injection) and [llm-attacks's repository](https://github.com/llm-attacks/llm-attacks). | | [Open-Prompt-Injection](/tools/liu00222-open-prompt-injection.md) | [llm-attacks](/tools/llm-attacks-llm-attacks.md) | | --- | --- | --- | | Tagline | Benchmark and toolkit for prompt injection attacks and defenses in LLMs | Universal and Transferable Attacks on Aligned Language Models | | Stars | 470 | 4,756 | | Forks | 74 | 633 | | Open issues | 14 | 69 | | 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. | llm-attacks: Universal and Transferable Attacks on Aligned Language Models with dependency on FastChat. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Evaluation & Observability, LLM Frameworks | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Open-Prompt-Injection](/tools/liu00222-open-prompt-injection.md) | [llm-attacks](/tools/llm-attacks-llm-attacks.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 279d | 732d | | Open issues (now) | 14 | 69 | | Owner type | User | Organization | | Full report | [trust report](/tools/liu00222-open-prompt-injection/trust.md) | [trust report](/tools/llm-attacks-llm-attacks/trust.md) | ## Shared compatibility - **Python**: [Open-Prompt-Injection](/tools/liu00222-open-prompt-injection.md) - Python runtime; [llm-attacks](/tools/llm-attacks-llm-attacks.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: llm-attacks - **Adopt for:** llm-attacks: Universal and Transferable Attacks on Aligned Language Models with dependency on FastChat. ## Choose when ### Choose Open-Prompt-Injection if… - Tags unique to Open-Prompt-Injection: llm, llm security, prompt-injection, security-and-privacy. - You prioritize security testing specifically for prompt injection vulnerabilities in your LLM applications. - More recently updated (last pushed Oct 29, 2025). ### Choose llm-attacks if… - Tags unique to llm-attacks: alignment-testing, attacks, fastchat-dependency, language-models. - When you need to test the robustness of aligned language models specifically using attacks designed for these systems, - More GitHub stars (4.8k vs 470) - visibility, not fit. ## 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 llm-attacks - Do not use if you are evaluating generic or unaligned language models without a need for alignment-specific attack testing, - Avoid when FastChat is not used in your project as llm-attacks explicitly depends on it. ## Common questions ### What is the difference between Open-Prompt-Injection and llm-attacks? Open-Prompt-Injection: Benchmark and toolkit for prompt injection attacks and defenses in LLMs. llm-attacks: Universal and Transferable Attacks on Aligned Language Models. See the comparison table for live GitHub stats and shared categories. ### When should I choose Open-Prompt-Injection over llm-attacks? Choose Open-Prompt-Injection over llm-attacks when Tags unique to Open-Prompt-Injection: llm, llm security, prompt-injection, security-and-privacy; You prioritize security testing specifically for prompt injection vulnerabilities in your LLM applications; More recently updated (last pushed Oct 29, 2025). ### When should I choose llm-attacks over Open-Prompt-Injection? Choose llm-attacks over Open-Prompt-Injection when Tags unique to llm-attacks: alignment-testing, attacks, fastchat-dependency, language-models; When you need to test the robustness of aligned language models specifically using attacks designed for these systems,; More GitHub stars (4.8k vs 470) - visibility, not fit. ### 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 llm-attacks? Do not use if you are evaluating generic or unaligned language models without a need for alignment-specific attack testing, Avoid when FastChat is not used in your project as llm-attacks explicitly depends on it. ### Is Open-Prompt-Injection or llm-attacks more popular on GitHub? llm-attacks has more GitHub stars (4,756 vs 470). Stars measure visibility, not whether either tool fits your constraints. ### Are Open-Prompt-Injection and llm-attacks open source? Yes - both are open-source projects on GitHub (Open-Prompt-Injection: MIT, llm-attacks: MIT). ### Where can I find alternatives to Open-Prompt-Injection or llm-attacks? GraphCanon lists graph-backed alternatives at [Open-Prompt-Injection alternatives](/tools/liu00222-open-prompt-injection/alternatives) and [llm-attacks alternatives](/tools/llm-attacks-llm-attacks/alternatives) ([Open-Prompt-Injection markdown twin](/tools/liu00222-open-prompt-injection/alternatives.md), [llm-attacks markdown twin](/tools/llm-attacks-llm-attacks/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-llm-attacks-llm-attacks.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 llm-attacks? Open-Prompt-Injection: Slowing. llm-attacks: Dormant. 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 llm-attacks? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Open-Prompt-Injection trust report](/tools/liu00222-open-prompt-injection/trust); [llm-attacks trust report](/tools/llm-attacks-llm-attacks/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/_