--- title: "Open-Prompt-Injection vs IB4LLMs" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/liu00222-open-prompt-injection-vs-zichuan-liu-ib4llms" tools: ["liu00222-open-prompt-injection", "zichuan-liu-ib4llms"] --- # Open-Prompt-Injection vs IB4LLMs *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 IB4LLMs if iB4LLMs (IBProtector) is an LLM jailbreak defense method using the Information Bottleneck principle to prevent adversarial prompts while preserving key information. [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. [IB4LLMs](https://zichuan-liu.github.io/projects/IBProtector/index.html) has 25 stars, 2 forks, and 4 open issues, last pushed Nov 7, 2024. Figures are from public GitHub metadata via [Open-Prompt-Injection's repository](https://github.com/liu00222/Open-Prompt-Injection) and [IB4LLMs's repository](https://github.com/zichuan-liu/IB4LLMs). | | [Open-Prompt-Injection](/tools/liu00222-open-prompt-injection.md) | [IB4LLMs](/tools/zichuan-liu-ib4llms.md) | | --- | --- | --- | | Tagline | Benchmark and toolkit for prompt injection attacks and defenses in LLMs | Protecting Your LLMs with Information Bottleneck | | Stars | 470 | 25 | | Forks | 74 | 2 | | Open issues | 14 | 4 | | 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. | IB4LLMs (IBProtector) is an LLM jailbreak defense method using the Information Bottleneck principle to prevent adversarial prompts while preserving key information. | | Persona | - | - | | Runtime | - | - | | License | 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) | [IB4LLMs](/tools/zichuan-liu-ib4llms.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 279d | 635d | | Open issues (now) | 14 | 4 | | Full report | [trust report](/tools/liu00222-open-prompt-injection/trust.md) | [trust report](/tools/zichuan-liu-ib4llms/trust.md) | ## Shared compatibility - **Python**: [Open-Prompt-Injection](/tools/liu00222-open-prompt-injection.md) - Python runtime; [IB4LLMs](/tools/zichuan-liu-ib4llms.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: IB4LLMs - **Pricing:** unknown - License information unavailable; specific model pricing not provided. - **Requirements:** Dependencies include datasets==2.14.5, torch==2.1.1, transformers==4.40.1 among others. - **Adopt for:** IB4LLMs (IBProtector) is an LLM jailbreak defense method using the Information Bottleneck principle to prevent adversarial prompts while preserving key information. ## 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 GitHub stars (470 vs 25) - visibility, not fit. ### Choose IB4LLMs if… - Pricing: License information unavailable; specific model pricing not provided.. - Requirements: Dependencies include datasets==2.14.5, torch==2.1.1, transformers==4.40.1 among others.. - Tags unique to IB4LLMs: evaluation scripts, finetuning, inference, information bottleneck. - When you need a specialized tool for guarding against jailbreaks in your language models without losing important data. ## 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 IB4LLMs - If you require a more generalized model protection approach that does not rely strictly on the Information Bottleneck principle. - When your environment lacks support for specific packages like `fschat==0.2.20` which is crucial and cannot be updated due to potential conflicts. ## Common questions ### What is the difference between Open-Prompt-Injection and IB4LLMs? Open-Prompt-Injection: Benchmark and toolkit for prompt injection attacks and defenses in LLMs. IB4LLMs: Protecting Your LLMs with Information Bottleneck. See the comparison table for live GitHub stats and shared categories. ### When should I choose Open-Prompt-Injection over IB4LLMs? Choose Open-Prompt-Injection over IB4LLMs 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 GitHub stars (470 vs 25) - visibility, not fit. ### When should I choose IB4LLMs over Open-Prompt-Injection? Choose IB4LLMs over Open-Prompt-Injection when Pricing: License information unavailable; specific model pricing not provided.; Requirements: Dependencies include datasets==2.14.5, torch==2.1.1, transformers==4.40.1 among others.; Tags unique to IB4LLMs: evaluation scripts, finetuning, inference, information bottleneck; When you need a specialized tool for guarding against jailbreaks in your language models without losing important data. ### 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 IB4LLMs? If you require a more generalized model protection approach that does not rely strictly on the Information Bottleneck principle. When your environment lacks support for specific packages like `fschat==0.2.20` which is crucial and cannot be updated due to potential conflicts. ### Is Open-Prompt-Injection or IB4LLMs more popular on GitHub? Open-Prompt-Injection has more GitHub stars (470 vs 25). Stars measure visibility, not whether either tool fits your constraints. ### Are Open-Prompt-Injection and IB4LLMs open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to Open-Prompt-Injection or IB4LLMs? GraphCanon lists graph-backed alternatives at [Open-Prompt-Injection alternatives](/tools/liu00222-open-prompt-injection/alternatives) and [IB4LLMs alternatives](/tools/zichuan-liu-ib4llms/alternatives) ([Open-Prompt-Injection markdown twin](/tools/liu00222-open-prompt-injection/alternatives.md), [IB4LLMs markdown twin](/tools/zichuan-liu-ib4llms/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-zichuan-liu-ib4llms.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 IB4LLMs? Open-Prompt-Injection: Slowing. IB4LLMs: 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 IB4LLMs? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Open-Prompt-Injection trust report](/tools/liu00222-open-prompt-injection/trust); [IB4LLMs trust report](/tools/zichuan-liu-ib4llms/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/_