--- title: "ALERT vs weak-to-strong" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/babelscape-alert-vs-xuandongzhao-weak-to-strong" tools: ["babelscape-alert", "xuandongzhao-weak-to-strong"] --- # ALERT vs weak-to-strong *GraphCanon updated Aug 9, 2026* ## Verdict Pick ALERT if aLERT is designed specifically for red-teaming based safety evaluation on large language models, using MIT licensed prompts and adversarial augmentation; pick weak-to-strong if weak-to-Strong is an inference-time attack exploiting smaller models to guide larger LLMs towards harmful output generation. [ALERT](https://arxiv.org/abs/2404.08676) reports 59 GitHub stars, 8 forks, and 0 open issues, last pushed Sep 20, 2024. [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 [ALERT's repository](https://github.com/Babelscape/ALERT) and [weak-to-strong's repository](https://github.com/XuandongZhao/weak-to-strong). | | [ALERT](/tools/babelscape-alert.md) | [weak-to-strong](/tools/xuandongzhao-weak-to-strong.md) | | --- | --- | --- | | Tagline | A Comprehensive Benchmark for Assessing Large Language Models' Safety Through Red Teaming | Novel Inference-Time Attack Leveraging Small Models to Guide Larger LLMs into Generating Harmful Outputs | | Stars | 59 | 90 | | Forks | 8 | 10 | | Open issues | 0 | 3 | | Language | Python | Python | | Adopt for | ALERT is designed specifically for red-teaming based safety evaluation on large language models, using MIT licensed prompts and adversarial augmentation. | Weak-to-Strong is an inference-time attack exploiting smaller models to guide larger LLMs towards harmful output generation. | | Persona | - | - | | Runtime | - | - | | License | Other | MIT | | Categories | Evaluation & Observability | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ALERT](/tools/babelscape-alert.md) | [weak-to-strong](/tools/xuandongzhao-weak-to-strong.md) | | --- | --- | --- | | Days since push | 687d | 459d | | Open issues (now) | 0 | 3 | | Owner type | Organization | User | | Full report | [trust report](/tools/babelscape-alert/trust.md) | [trust report](/tools/xuandongzhao-weak-to-strong/trust.md) | ## Decision facts: ALERT - **Adopt for:** ALERT is designed specifically for red-teaming based safety evaluation on large language models, using MIT licensed prompts and adversarial augmentation. ## 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 ALERT if… - License: ALERT is Other, weak-to-strong is MIT. - Tags unique to ALERT: ai, artificial-intelligence, benchmark, bias-detection. - Also covers Evaluation & Observability. - When evaluating safety metrics of large language models through red-teaming approaches ### Choose weak-to-strong if… - License: weak-to-strong is MIT, ALERT is Other. - 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. - Also covers Inference & Serving. - 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 ALERT - If your evaluation does not require bias detection or safety assessment under adversarial conditions - In scenarios where a broader range of model aspects beyond safety is needed, as ALERT focuses primarily on safety benchmarks ## 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 ALERT and weak-to-strong? ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety Through Red Teaming. 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 ALERT over weak-to-strong? Choose ALERT over weak-to-strong when License: ALERT is Other, weak-to-strong is MIT; Tags unique to ALERT: ai, artificial-intelligence, benchmark, bias-detection; Also covers Evaluation & Observability; When evaluating safety metrics of large language models through red-teaming approaches. ### When should I choose weak-to-strong over ALERT? Choose weak-to-strong over ALERT when License: weak-to-strong is MIT, ALERT is Other; 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; Also covers Inference & Serving; 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 ALERT? If your evaluation does not require bias detection or safety assessment under adversarial conditions In scenarios where a broader range of model aspects beyond safety is needed, as ALERT focuses primarily on safety benchmarks ### 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 ALERT or weak-to-strong more popular on GitHub? weak-to-strong has more GitHub stars (90 vs 59). Stars measure visibility, not whether either tool fits your constraints. ### Are ALERT and weak-to-strong open source? Yes - both are open-source projects on GitHub (ALERT: Other, weak-to-strong: MIT). ### Where can I find alternatives to ALERT or weak-to-strong? GraphCanon lists graph-backed alternatives at [ALERT alternatives](/tools/babelscape-alert/alternatives) and [weak-to-strong alternatives](/tools/xuandongzhao-weak-to-strong/alternatives) ([ALERT markdown twin](/tools/babelscape-alert/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/babelscape-alert-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, ALERT or weak-to-strong? ALERT: Dormant. 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 ALERT and weak-to-strong? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ALERT trust report](/tools/babelscape-alert/trust); [weak-to-strong trust report](/tools/xuandongzhao-weak-to-strong/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=babelscape-alert`](/api/graphcanon/graph?tool=babelscape-alert) - 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/_