Comparison
baseline-defenses vs AutoDefense
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.
Markdown twin · baseline-defenses alternatives · AutoDefense alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | baseline-defenses | AutoDefense |
|---|---|---|
| Maintenance | Dormant (1013d since push) As of 2w · github_public_v1 | Slowing (201d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) 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
- baseline-defenses
- Research code for evaluating defenses against adversarial attacks on aligned language models
- AutoDefense
- Multi-Agent LLM Defense against Jailbreak Attacks
Stars
- baseline-defenses
- 34
- AutoDefense
- 68
Forks
- baseline-defenses
- 1
- AutoDefense
- 20
Open issues
- baseline-defenses
- 0
- AutoDefense
- 1
Language
- baseline-defenses
- Python
- AutoDefense
- Python
Adopt for
- baseline-defenses
- A toolkit for evaluating defenses against adversarial attacks on aligned language models, focusing on perplexity filter and paraphrase defense strategies.
- AutoDefense
- AutoDefense uses a multi-agent framework to mitigate jailbreak attacks on LLMs, installed via Python.
Persona
- baseline-defenses
- -
- AutoDefense
- -
Runtime
- baseline-defenses
- -
- AutoDefense
- -
License
- baseline-defenses
- -
- AutoDefense
- MIT
Last pushed
- baseline-defenses
- Oct 26, 2023
- AutoDefense
- Jan 15, 2026
Categories
- baseline-defenses
- Evaluation & Observability
- AutoDefense
- AI Agents, Evaluation & Observability
Trust and health
Maintenance
- baseline-defenses
- Dormant (18%)
- AutoDefense
- Slowing (36%)
Days since push
- baseline-defenses
- 1013d
- AutoDefense
- 201d
Open issues (now)
- baseline-defenses
- 0
- AutoDefense
- 1
Full report
- baseline-defenses
- Trust report
- AutoDefense
- Trust report
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).
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.
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 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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (neelsjain/baseline-defenses) · observed Aug 5, 2026
- GitHub forks (neelsjain/baseline-defenses) · observed Aug 5, 2026
- Last push (neelsjain/baseline-defenses) · observed Oct 26, 2023
- License file (unknown) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (XHMY/AutoDefense) · observed Aug 5, 2026
- GitHub forks (XHMY/AutoDefense) · observed Aug 5, 2026
- Last push (XHMY/AutoDefense) · observed Jan 15, 2026
- License file (MIT) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: baseline-defenses 34 · AutoDefense 68 (synced Aug 5, 2026).
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 and AutoDefense alternatives (baseline-defenses markdown twin, AutoDefense 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, 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; AutoDefense trust report.