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Comparison

llm-attacks vs GPTFuzz

Verdict

Pick llm-attacks if llm-attacks: Universal and Transferable Attacks on Aligned Language Models with dependency on FastChat; pick GPTFuzz if gPTFuzz leverages auto-generated jailbreak prompts to red team large language models for testing and evaluation.

Markdown twin · llm-attacks alternatives · GPTFuzz alternatives

GraphCanon updated 2w

llm-attacks logo

llm-attacks

llm-attacks/llm-attacks

★ 4.8kpushed Aug 2, 2024
vs
GPTFuzz logo

GPTFuzz

sherdencooper/GPTFuzz

★ 604pushed Feb 27, 2026

Trust & integrity

Signalllm-attacksGPTFuzz
Maintenance
Dormant (732d since push)
As of 2w · github_public_v1
Slowing (158d since push)
As of 2w · github_public_v1
Provenance
Not a fork · Organization account
As of 2w · github_public_v1
Not a fork · Personal account
As of 2w · github_public_v1
OSV dependency advisories
Published findings
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
llm-attacks trust reportGPTFuzz trust reportTrust methodology

Tagline

llm-attacks
Universal and Transferable Attacks on Aligned Language Models
GPTFuzz
Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts

Stars

llm-attacks
4.8k
GPTFuzz
604

Forks

llm-attacks
633
GPTFuzz
87

Open issues

llm-attacks
69
GPTFuzz
17

Language

llm-attacks
Python
GPTFuzz
Python

Adopt for

llm-attacks
llm-attacks: Universal and Transferable Attacks on Aligned Language Models with dependency on FastChat.
GPTFuzz
GPTFuzz leverages auto-generated jailbreak prompts to red team large language models for testing and evaluation.

Persona

llm-attacks
-
GPTFuzz
-

Runtime

llm-attacks
-
GPTFuzz
-

License

llm-attacks
MIT
GPTFuzz
MIT

Last pushed

llm-attacks
Aug 2, 2024
GPTFuzz
Feb 27, 2026

Categories

llm-attacks
Evaluation & Observability, LLM Frameworks
GPTFuzz
Evaluation & Observability, LLM Frameworks

Trust and health

Maintenance

llm-attacks
Dormant (18%)
GPTFuzz
Slowing (36%)

Days since push

llm-attacks
732d
GPTFuzz
158d

Open issues (now)

llm-attacks
69
GPTFuzz
17

Owner type

llm-attacks
Organization
GPTFuzz
User

OSV dependency advisories

llm-attacks
Published findings
GPTFuzz
No lockfile (source not queried)

Full report

llm-attacks
Trust report
GPTFuzz
Trust report

Choose llm-attacks if…

  • Tags unique to llm-attacks: alignment-testing, attacks, fastchat-dependency, language-models.
  • When you need to test the robustness of aligned language models specifically using attacks designed for these systems,
  • More GitHub stars (4.8k vs 604) - visibility, not fit.

When NOT to use llm-attacks

  • Do not use if you are evaluating generic or unaligned language models without a need for alignment-specific attack testing,
  • Avoid when FastChat is not used in your project as llm-attacks explicitly depends on it.
GitHubDetails

Choose GPTFuzz if…

  • Tags unique to GPTFuzz: jailbreak prompts, large language models, red-teaming.
  • When you need to test the robustness of LLMs against potential manipulative input designed to bypass content controls.
  • More recently updated (last pushed Feb 27, 2026).

When NOT to use GPTFuzz

  • If your project requires straightforward, uncontroversial testing tools that do not engage with sensitive content control evasion techniques.
  • For general-purpose debugging and optimization tasks where red teaming tactics are not necessary or appropriate.
GitHubDetails

Explore

llm-attacks trust report →GPTFuzz trust report →Evaluation & Observability category →LLM Frameworks category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: llm-attacks 4.8k · GPTFuzz 604 (synced Aug 5, 2026).

Common questions

What is the difference between llm-attacks and GPTFuzz?
llm-attacks: Universal and Transferable Attacks on Aligned Language Models. GPTFuzz: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts. See the comparison table for live GitHub stats and shared categories.
When should I choose llm-attacks over GPTFuzz?
Choose llm-attacks over GPTFuzz when Tags unique to llm-attacks: alignment-testing, attacks, fastchat-dependency, language-models; When you need to test the robustness of aligned language models specifically using attacks designed for these systems,; More GitHub stars (4.8k vs 604) - visibility, not fit.
When should I choose GPTFuzz over llm-attacks?
Choose GPTFuzz over llm-attacks when Tags unique to GPTFuzz: jailbreak prompts, large language models, red-teaming; When you need to test the robustness of LLMs against potential manipulative input designed to bypass content controls; More recently updated (last pushed Feb 27, 2026).
When should I avoid llm-attacks?
Do not use if you are evaluating generic or unaligned language models without a need for alignment-specific attack testing, Avoid when FastChat is not used in your project as llm-attacks explicitly depends on it.
When should I avoid GPTFuzz?
If your project requires straightforward, uncontroversial testing tools that do not engage with sensitive content control evasion techniques. For general-purpose debugging and optimization tasks where red teaming tactics are not necessary or appropriate.
Is llm-attacks or GPTFuzz more popular on GitHub?
llm-attacks has more GitHub stars (4,756 vs 604). Stars measure visibility, not whether either tool fits your constraints.
Are llm-attacks and GPTFuzz open source?
Yes - both are open-source projects on GitHub (llm-attacks: MIT, GPTFuzz: MIT).
Where can I find alternatives to llm-attacks or GPTFuzz?
GraphCanon lists graph-backed alternatives at llm-attacks alternatives and GPTFuzz alternatives (llm-attacks markdown twin, GPTFuzz 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, llm-attacks or GPTFuzz?
llm-attacks: Dormant. GPTFuzz: 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 llm-attacks and GPTFuzz?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: llm-attacks trust report; GPTFuzz trust report.

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