--- title: "ReNeLLM vs weak-to-strong" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/njunlp-renellm-vs-xuandongzhao-weak-to-strong" tools: ["njunlp-renellm", "xuandongzhao-weak-to-strong"] --- # ReNeLLM vs weak-to-strong *GraphCanon updated Aug 5, 2026* ## 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. [ReNeLLM](https://github.com/NJUNLP/ReNeLLM) reports 163 GitHub stars, 17 forks, and 0 open issues, last pushed Sep 2, 2025. [weak-to-strong](https://github.com/XuandongZhao/weak-to-strong) has 90 stars, 10 forks, and 3 open issues, last pushed May 2, 2025. Figures are from public GitHub metadata via [ReNeLLM's repository](https://github.com/NJUNLP/ReNeLLM) and [weak-to-strong's repository](https://github.com/XuandongZhao/weak-to-strong). | | [ReNeLLM](/tools/njunlp-renellm.md) | [weak-to-strong](/tools/xuandongzhao-weak-to-strong.md) | | --- | --- | --- | | Tagline | Implementation of generalized nested jailbreak prompts targeting large language models. | Novel Inference-Time Attack Leveraging Small Models to Guide Larger LLMs into Generating Harmful Outputs | | Stars | 163 | 90 | | Forks | 17 | 10 | | Open issues | 0 | 3 | | Language | Python | Python | | Adopt for | 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 is an inference-time attack exploiting smaller models to guide larger LLMs towards harmful output generation. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Evaluation & Observability, Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ReNeLLM](/tools/njunlp-renellm.md) | [weak-to-strong](/tools/xuandongzhao-weak-to-strong.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 336d | 459d | | Open issues (now) | 0 | 3 | | Owner type | Organization | User | | Full report | [trust report](/tools/njunlp-renellm/trust.md) | [trust report](/tools/xuandongzhao-weak-to-strong/trust.md) | ## Shared compatibility - **Python**: [ReNeLLM](/tools/njunlp-renellm.md) - Python runtime; [weak-to-strong](/tools/xuandongzhao-weak-to-strong.md) - Python runtime ## Decision facts: ReNeLLM - **Adopt for:** ReNeLLM is an implementation of generalized nested jailbreak prompts targeting large language models such as gpt-3.5-turbo and claude-v2. ## Decision facts: weak-to-strong - **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. - **Adopt for:** Weak-to-Strong is an inference-time attack exploiting smaller models to guide larger LLMs towards harmful output generation. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/njunlp-renellm/alternatives) and [weak-to-strong alternatives](/tools/xuandongzhao-weak-to-strong/alternatives) ([ReNeLLM markdown twin](/tools/njunlp-renellm/alternatives.md), [weak-to-strong markdown twin](/tools/xuandongzhao-weak-to-strong/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/njunlp-renellm-vs-xuandongzhao-weak-to-strong.md) 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](/tools/njunlp-renellm/trust); [weak-to-strong trust report](/tools/xuandongzhao-weak-to-strong/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=njunlp-renellm`](/api/graphcanon/graph?tool=njunlp-renellm) - 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/_