---
title: "llm-attacks vs GPTFuzz"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/llm-attacks-llm-attacks-vs-sherdencooper-gptfuzz"
tools: ["llm-attacks-llm-attacks", "sherdencooper-gptfuzz"]
---

# llm-attacks vs GPTFuzz

*GraphCanon updated Aug 5, 2026*

## 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.

[llm-attacks](https://llm-attacks.org/) reports 4.8k GitHub stars, 633 forks, and 69 open issues, last pushed Aug 2, 2024. [GPTFuzz](https://github.com/sherdencooper/GPTFuzz) has 604 stars, 87 forks, and 17 open issues, last pushed Feb 27, 2026. Figures are from public GitHub metadata via [llm-attacks's repository](https://github.com/llm-attacks/llm-attacks) and [GPTFuzz's repository](https://github.com/sherdencooper/GPTFuzz).

| | [llm-attacks](/tools/llm-attacks-llm-attacks.md) | [GPTFuzz](/tools/sherdencooper-gptfuzz.md) |
| --- | --- | --- |
| Tagline | Universal and Transferable Attacks on Aligned Language Models | Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts |
| Stars | 4,756 | 604 |
| Forks | 633 | 87 |
| Open issues | 69 | 17 |
| Language | Python | Python |
| Adopt for | llm-attacks: Universal and Transferable Attacks on Aligned Language Models with dependency on FastChat. | GPTFuzz leverages auto-generated jailbreak prompts to red team large language models for testing and evaluation. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | MIT |
| Categories | Evaluation & Observability, LLM Frameworks | Evaluation & Observability, LLM Frameworks |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [llm-attacks](/tools/llm-attacks-llm-attacks.md) | [GPTFuzz](/tools/sherdencooper-gptfuzz.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Slowing (36%) |
| Days since push | 732d | 158d |
| Open issues (now) | 69 | 17 |
| Owner type | Organization | User |
| Full report | [trust report](/tools/llm-attacks-llm-attacks/trust.md) | [trust report](/tools/sherdencooper-gptfuzz/trust.md) |

## Decision facts: llm-attacks

- **Adopt for:** llm-attacks: Universal and Transferable Attacks on Aligned Language Models with dependency on FastChat.

## Decision facts: GPTFuzz

- **Adopt for:** GPTFuzz leverages auto-generated jailbreak prompts to red team large language models for testing and evaluation.

## Choose when

### 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.

### 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 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 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.

## 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](/tools/llm-attacks-llm-attacks/alternatives) and [GPTFuzz alternatives](/tools/sherdencooper-gptfuzz/alternatives) ([llm-attacks markdown twin](/tools/llm-attacks-llm-attacks/alternatives.md), [GPTFuzz markdown twin](/tools/sherdencooper-gptfuzz/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/llm-attacks-llm-attacks-vs-sherdencooper-gptfuzz.md) 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](/tools/llm-attacks-llm-attacks/trust); [GPTFuzz trust report](/tools/sherdencooper-gptfuzz/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=llm-attacks-llm-attacks`](/api/graphcanon/graph?tool=llm-attacks-llm-attacks)
- 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/_