Comparison
Open-Prompt-Injection vs IB4LLMs
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.
Markdown twin · Open-Prompt-Injection alternatives · IB4LLMs alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | Open-Prompt-Injection | IB4LLMs |
|---|---|---|
| Maintenance | Slowing (279d since push) As of 3w · github_public_v1 | Dormant (635d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Personal account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- Open-Prompt-Injection
- Benchmark and toolkit for prompt injection attacks and defenses in LLMs
- IB4LLMs
- Protecting Your LLMs with Information Bottleneck
Stars
- Open-Prompt-Injection
- 470
- IB4LLMs
- 25
Forks
- Open-Prompt-Injection
- 74
- IB4LLMs
- 2
Open issues
- Open-Prompt-Injection
- 14
- IB4LLMs
- 4
Language
- Open-Prompt-Injection
- Python
- IB4LLMs
- Python
Adopt for
- Open-Prompt-Injection
- 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
- IB4LLMs (IBProtector) is an LLM jailbreak defense method using the Information Bottleneck principle to prevent adversarial prompts while preserving key information.
Persona
- Open-Prompt-Injection
- -
- IB4LLMs
- -
Runtime
- Open-Prompt-Injection
- -
- IB4LLMs
- -
License
- Open-Prompt-Injection
- MIT
- IB4LLMs
- -
Last pushed
- Open-Prompt-Injection
- Oct 29, 2025
- IB4LLMs
- Nov 7, 2024
Categories
- Open-Prompt-Injection
- Evaluation & Observability, LLM Frameworks
- IB4LLMs
- Evaluation & Observability, LLM Frameworks
Trust and health
Maintenance
- Open-Prompt-Injection
- Slowing (36%)
- IB4LLMs
- Dormant (18%)
Days since push
- Open-Prompt-Injection
- 279d
- IB4LLMs
- 635d
Open issues (now)
- Open-Prompt-Injection
- 14
- IB4LLMs
- 4
OSV dependency advisories
- Open-Prompt-Injection
- No lockfile (source not queried)
- IB4LLMs
- Published findings
Full report
- Open-Prompt-Injection
- Trust report
- IB4LLMs
- Trust report
Shared compatibility
- Python · Open-Prompt-Injection: Python runtime · IB4LLMs: Python runtime
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (liu00222/Open-Prompt-Injection) · observed Aug 5, 2026
- GitHub forks (liu00222/Open-Prompt-Injection) · observed Aug 5, 2026
- Last push (liu00222/Open-Prompt-Injection) · observed Oct 29, 2025
- License file (MIT) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (zichuan-liu/IB4LLMs) · observed Aug 5, 2026
- GitHub forks (zichuan-liu/IB4LLMs) · observed Aug 5, 2026
- Last push (zichuan-liu/IB4LLMs) · observed Nov 7, 2024
- License file (unknown) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Open-Prompt-Injection 470 · IB4LLMs 25 (synced Aug 5, 2026).
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==0.2.20which 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 and IB4LLMs alternatives (Open-Prompt-Injection markdown twin, IB4LLMs markdown twin), 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 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; IB4LLMs trust report.