Comparison
ReNeLLM vs weak-to-strong
Verdict
Pick ReNeLLM if reNeLLM is an implementation of generalized nested jailbreak prompts targeting large language models such as gpt-3.5-turbo and claude-v2; pick weak-to-strong if weak-to-Strong is an inference-time attack exploiting smaller models to guide larger LLMs towards harmful output generation.
Markdown twin · ReNeLLM alternatives · weak-to-strong alternatives
GraphCanon updated 3w
Trust & integrity
| Signal | ReNeLLM | weak-to-strong |
|---|---|---|
| Maintenance | Slowing (336d since push) As of 3w · github_public_v1 | Dormant (459d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Personal account As of 3w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | No lockfile (source not queried) 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
- ReNeLLM
- Implementation of generalized nested jailbreak prompts targeting large language models.
- weak-to-strong
- Novel Inference-Time Attack Leveraging Small Models to Guide Larger LLMs into Generating Harmful Outputs
Stars
- ReNeLLM
- 163
- weak-to-strong
- 90
Forks
- ReNeLLM
- 17
- weak-to-strong
- 10
Open issues
- ReNeLLM
- 0
- weak-to-strong
- 3
Language
- ReNeLLM
- Python
- weak-to-strong
- Python
Adopt for
- ReNeLLM
- ReNeLLM is an implementation of generalized nested jailbreak prompts targeting large language models such as gpt-3.5-turbo and claude-v2.
- weak-to-strong
- Weak-to-Strong is an inference-time attack exploiting smaller models to guide larger LLMs towards harmful output generation.
Persona
- ReNeLLM
- -
- weak-to-strong
- -
Runtime
- ReNeLLM
- -
- weak-to-strong
- -
License
- ReNeLLM
- MIT
- weak-to-strong
- MIT
Last pushed
- ReNeLLM
- Sep 2, 2025
- weak-to-strong
- May 2, 2025
Categories
- ReNeLLM
- Evaluation & Observability, Inference & Serving
- weak-to-strong
- Inference & Serving
Trust and health
Maintenance
- ReNeLLM
- Slowing (36%)
- weak-to-strong
- Dormant (18%)
Days since push
- ReNeLLM
- 336d
- weak-to-strong
- 459d
Open issues (now)
- ReNeLLM
- 0
- weak-to-strong
- 3
Owner type
- ReNeLLM
- Organization
- weak-to-strong
- User
OSV dependency advisories
- ReNeLLM
- Published findings
- weak-to-strong
- No lockfile (source not queried)
Full report
- ReNeLLM
- Trust report
- weak-to-strong
- Trust report
Shared compatibility
- Python · ReNeLLM: Python runtime · weak-to-strong: Python runtime
Choose ReNeLLM if…
- Tags unique to ReNeLLM: api interaction, jailbreak prompts, language model evaluation, model reliability assessment.
- Also covers Evaluation & Observability.
- When you aim to evaluate the susceptibility of LLMs like gpt-3.5-turbo and claude-v2 to deception or jailbroken prompts.
When NOT to use ReNeLLM
- When you wish to develop applications that strictly adhere to ethical guidelines and do not involve the testing of harmful prompts.
- If your focus is on building production-ready LLM-based services without interest in evaluating security or adversarial aspects of these models.
Choose weak-to-strong if…
- Requirements: Min 8 GB RAM; The smaller models guiding the large LLM must be available.; A high-performance computing environment might be necessary if running on very large datasets or models..
- Tags unique to weak-to-strong: inference-time attack, jailbreaking, large language models.
- Use it for research purposes specifically geared at understanding the vulnerabilities in large language models and improving their robustness against adversarial attacks.
When NOT to use weak-to-strong
- Do not use it for applications requiring ethical guidelines adherence as it is designed to navigate around the safety mechanisms in large language models.
- Avoid using this tool if you are developing systems that must ensure consistent alignment and prevent any form of harmful output generation, such as public communication platforms or education tools.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (NJUNLP/ReNeLLM) · observed Aug 5, 2026
- GitHub forks (NJUNLP/ReNeLLM) · observed Aug 5, 2026
- Last push (NJUNLP/ReNeLLM) · observed Sep 2, 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 (XuandongZhao/weak-to-strong) · observed Aug 5, 2026
- GitHub forks (XuandongZhao/weak-to-strong) · observed Aug 5, 2026
- Last push (XuandongZhao/weak-to-strong) · observed May 2, 2025
- License file (MIT) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: ReNeLLM 163 · weak-to-strong 90 (synced Aug 5, 2026).
Common questions
- What is the difference between ReNeLLM and weak-to-strong?
- ReNeLLM: Implementation of generalized nested jailbreak prompts targeting large language models.. weak-to-strong: Novel Inference-Time Attack Leveraging Small Models to Guide Larger LLMs into Generating Harmful Outputs. See the comparison table for live GitHub stats and shared categories.
- When should I choose ReNeLLM over weak-to-strong?
- Choose ReNeLLM over weak-to-strong when Tags unique to ReNeLLM: api interaction, jailbreak prompts, language model evaluation, model reliability assessment; Also covers Evaluation & Observability; When you aim to evaluate the susceptibility of LLMs like gpt-3.5-turbo and claude-v2 to deception or jailbroken prompts.
- When should I choose weak-to-strong over ReNeLLM?
- Choose weak-to-strong over ReNeLLM when Requirements: Min 8 GB RAM; The smaller models guiding the large LLM must be available.; A high-performance computing environment might be necessary if running on very large datasets or models.; Tags unique to weak-to-strong: inference-time attack, jailbreaking, large language models; Use it for research purposes specifically geared at understanding the vulnerabilities in large language models and improving their robustness against adversarial attacks.
- When should I avoid ReNeLLM?
- When you wish to develop applications that strictly adhere to ethical guidelines and do not involve the testing of harmful prompts. If your focus is on building production-ready LLM-based services without interest in evaluating security or adversarial aspects of these models.
- When should I avoid weak-to-strong?
- Do not use it for applications requiring ethical guidelines adherence as it is designed to navigate around the safety mechanisms in large language models. Avoid using this tool if you are developing systems that must ensure consistent alignment and prevent any form of harmful output generation, such as public communication platforms or education tools.
- Is ReNeLLM or weak-to-strong more popular on GitHub?
- ReNeLLM has more GitHub stars (163 vs 90). Stars measure visibility, not whether either tool fits your constraints.
- Are ReNeLLM and weak-to-strong open source?
- Yes - both are open-source projects on GitHub (ReNeLLM: MIT, weak-to-strong: MIT).
- Where can I find alternatives to ReNeLLM or weak-to-strong?
- GraphCanon lists graph-backed alternatives at ReNeLLM alternatives and weak-to-strong alternatives (ReNeLLM markdown twin, weak-to-strong 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, ReNeLLM or weak-to-strong?
- ReNeLLM: Slowing. weak-to-strong: 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 ReNeLLM and weak-to-strong?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ReNeLLM trust report; weak-to-strong trust report.