--- title: "baseline-defenses vs AutoDefense" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/neelsjain-baseline-defenses-vs-xhmy-autodefense" tools: ["neelsjain-baseline-defenses", "xhmy-autodefense"] --- # baseline-defenses vs AutoDefense *GraphCanon updated Aug 5, 2026* ## Verdict Pick baseline-defenses if a toolkit for evaluating defenses against adversarial attacks on aligned language models, focusing on perplexity filter and paraphrase defense strategies; pick AutoDefense if autoDefense uses a multi-agent framework to mitigate jailbreak attacks on LLMs, installed via Python. [baseline-defenses](https://github.com/neelsjain/baseline-defenses) reports 34 GitHub stars, 1 forks, and 0 open issues, last pushed Oct 26, 2023. [AutoDefense](https://arxiv.org/abs/2403.04783) has 68 stars, 20 forks, and 1 open issues, last pushed Jan 15, 2026. Figures are from public GitHub metadata via [baseline-defenses's repository](https://github.com/neelsjain/baseline-defenses) and [AutoDefense's repository](https://github.com/XHMY/AutoDefense). | | [baseline-defenses](/tools/neelsjain-baseline-defenses.md) | [AutoDefense](/tools/xhmy-autodefense.md) | | --- | --- | --- | | Tagline | Research code for evaluating defenses against adversarial attacks on aligned language models | Multi-Agent LLM Defense against Jailbreak Attacks | | Stars | 34 | 68 | | Forks | 1 | 20 | | Open issues | 0 | 1 | | Language | Python | Python | | Adopt for | A toolkit for evaluating defenses against adversarial attacks on aligned language models, focusing on perplexity filter and paraphrase defense strategies. | AutoDefense uses a multi-agent framework to mitigate jailbreak attacks on LLMs, installed via Python. | | Persona | - | - | | Runtime | - | - | | License | - | MIT | | Categories | Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [baseline-defenses](/tools/neelsjain-baseline-defenses.md) | [AutoDefense](/tools/xhmy-autodefense.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 1013d | 201d | | Open issues (now) | 0 | 1 | | Full report | [trust report](/tools/neelsjain-baseline-defenses/trust.md) | [trust report](/tools/xhmy-autodefense/trust.md) | ## Decision facts: baseline-defenses - **Adopt for:** A toolkit for evaluating defenses against adversarial attacks on aligned language models, focusing on perplexity filter and paraphrase defense strategies. ## Decision facts: AutoDefense - **Adopt for:** AutoDefense uses a multi-agent framework to mitigate jailbreak attacks on LLMs, installed via Python. ## Choose when ### Choose baseline-defenses if… - Tags unique to baseline-defenses: adversarial-attacks, defense strategies, paraphrase defense, perplexity filter. - - When you need to evaluate the effectiveness of baseline defenses such as the perplexity filter or paraphrase defense in protecting aligned language models from adversarial attacks. - Leaner open-issue backlog (0). ### Choose AutoDefense if… - Tags unique to AutoDefense: defense-mechanism, jailbreak prevention, large language models, llm-defense. - Also covers AI Agents. - Implementing robust defenses for enterprise-level AI projects with high-security requirements ## When NOT to use baseline-defenses - - Do not use if you require comprehensive coverage of all possible defensive measures. This tool specifically lacks detailed code for retokenization defenses involving BPE-dropout. - - If your scenario demands more advanced or specialized defense mechanisms beyond the scope of baseline strategies, this repository will fall short on delivering those. ## When NOT to use AutoDefense - Projects requiring light-weight solutions where multi-agent systems might introduce complexity overhead - Environments without access to Python and its ecosystem, as AutoDefense depends on specific Python packages ## Common questions ### What is the difference between baseline-defenses and AutoDefense? baseline-defenses: Research code for evaluating defenses against adversarial attacks on aligned language models. AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks. See the comparison table for live GitHub stats and shared categories. ### When should I choose baseline-defenses over AutoDefense? Choose baseline-defenses over AutoDefense when Tags unique to baseline-defenses: adversarial-attacks, defense strategies, paraphrase defense, perplexity filter; - When you need to evaluate the effectiveness of baseline defenses such as the perplexity filter or paraphrase defense in protecting aligned language models from adversarial attacks; Leaner open-issue backlog (0). ### When should I choose AutoDefense over baseline-defenses? Choose AutoDefense over baseline-defenses when Tags unique to AutoDefense: defense-mechanism, jailbreak prevention, large language models, llm-defense; Also covers AI Agents; Implementing robust defenses for enterprise-level AI projects with high-security requirements. ### When should I avoid baseline-defenses? - Do not use if you require comprehensive coverage of all possible defensive measures. This tool specifically lacks detailed code for retokenization defenses involving BPE-dropout. - If your scenario demands more advanced or specialized defense mechanisms beyond the scope of baseline strategies, this repository will fall short on delivering those. ### When should I avoid AutoDefense? Projects requiring light-weight solutions where multi-agent systems might introduce complexity overhead Environments without access to Python and its ecosystem, as AutoDefense depends on specific Python packages ### Is baseline-defenses or AutoDefense more popular on GitHub? AutoDefense has more GitHub stars (68 vs 34). Stars measure visibility, not whether either tool fits your constraints. ### Are baseline-defenses and AutoDefense open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to baseline-defenses or AutoDefense? GraphCanon lists graph-backed alternatives at [baseline-defenses alternatives](/tools/neelsjain-baseline-defenses/alternatives) and [AutoDefense alternatives](/tools/xhmy-autodefense/alternatives) ([baseline-defenses markdown twin](/tools/neelsjain-baseline-defenses/alternatives.md), [AutoDefense markdown twin](/tools/xhmy-autodefense/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/neelsjain-baseline-defenses-vs-xhmy-autodefense.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, baseline-defenses or AutoDefense? baseline-defenses: Dormant. AutoDefense: Slowing. 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 baseline-defenses and AutoDefense? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [baseline-defenses trust report](/tools/neelsjain-baseline-defenses/trust); [AutoDefense trust report](/tools/xhmy-autodefense/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=neelsjain-baseline-defenses`](/api/graphcanon/graph?tool=neelsjain-baseline-defenses) - 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/_