--- title: "agentdojo vs weak-to-strong" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ethz-spylab-agentdojo-vs-xuandongzhao-weak-to-strong" tools: ["ethz-spylab-agentdojo", "xuandongzhao-weak-to-strong"] --- # agentdojo vs weak-to-strong *GraphCanon updated Aug 5, 2026* ## Verdict Pick agentdojo if agentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents; pick weak-to-strong if weak-to-Strong is an inference-time attack exploiting smaller models to guide larger LLMs towards harmful output generation. [agentdojo](https://agentdojo.spylab.ai/) reports 716 GitHub stars, 188 forks, and 41 open issues, last pushed Jun 2, 2026. [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 [agentdojo's repository](https://github.com/ethz-spylab/agentdojo) and [weak-to-strong's repository](https://github.com/XuandongZhao/weak-to-strong). | | [agentdojo](/tools/ethz-spylab-agentdojo.md) | [weak-to-strong](/tools/xuandongzhao-weak-to-strong.md) | | --- | --- | --- | | Tagline | A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents | Novel Inference-Time Attack Leveraging Small Models to Guide Larger LLMs into Generating Harmful Outputs | | Stars | 716 | 90 | | Forks | 188 | 10 | | Open issues | 41 | 3 | | Language | Python | Python | | Adopt for | AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. | 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 | AI Agents, Evaluation & Observability | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentdojo](/tools/ethz-spylab-agentdojo.md) | [weak-to-strong](/tools/xuandongzhao-weak-to-strong.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 63d | 459d | | Open issues (now) | 41 | 3 | | Owner type | Organization | User | | Full report | [trust report](/tools/ethz-spylab-agentdojo/trust.md) | [trust report](/tools/xuandongzhao-weak-to-strong/trust.md) | ## Shared compatibility - **Python**: [agentdojo](/tools/ethz-spylab-agentdojo.md) - Python runtime; [weak-to-strong](/tools/xuandongzhao-weak-to-strong.md) - Python runtime ## Decision facts: agentdojo - **Pricing:** freemium - Open-source under the MIT License. Some advanced features might require additional libraries or APIs. - **Requirements:** Min 8 GB RAM - **Adopt for:** AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. ## 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 agentdojo if… - Pricing: Open-source under the MIT License. Some advanced features might require additional libraries or APIs.. - Requirements: Min 8 GB RAM. - Tags unique to agentdojo: benchmark, prompt-injection, security. - Also covers AI Agents, Evaluation & Observability. - AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. ### 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. - 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 agentdojo - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## 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 agentdojo and weak-to-strong? agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. 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 agentdojo over weak-to-strong? Choose agentdojo over weak-to-strong when Pricing: Open-source under the MIT License. Some advanced features might require additional libraries or APIs.; Requirements: Min 8 GB RAM; Tags unique to agentdojo: benchmark, prompt-injection, security; Also covers AI Agents, Evaluation & Observability; AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. ### When should I choose weak-to-strong over agentdojo? Choose weak-to-strong over agentdojo 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; 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 agentdojo? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### 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 agentdojo or weak-to-strong more popular on GitHub? agentdojo has more GitHub stars (716 vs 90). Stars measure visibility, not whether either tool fits your constraints. ### Are agentdojo and weak-to-strong open source? Yes - both are open-source projects on GitHub (agentdojo: MIT, weak-to-strong: MIT). ### Where can I find alternatives to agentdojo or weak-to-strong? GraphCanon lists graph-backed alternatives at [agentdojo alternatives](/tools/ethz-spylab-agentdojo/alternatives) and [weak-to-strong alternatives](/tools/xuandongzhao-weak-to-strong/alternatives) ([agentdojo markdown twin](/tools/ethz-spylab-agentdojo/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/ethz-spylab-agentdojo-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, agentdojo or weak-to-strong? agentdojo: Steady. 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 agentdojo and weak-to-strong? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentdojo trust report](/tools/ethz-spylab-agentdojo/trust); [weak-to-strong trust report](/tools/xuandongzhao-weak-to-strong/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ethz-spylab-agentdojo`](/api/graphcanon/graph?tool=ethz-spylab-agentdojo) - 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/_