---
title: "ALERT vs llm-attacks"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/babelscape-alert-vs-llm-attacks-llm-attacks"
tools: ["babelscape-alert", "llm-attacks-llm-attacks"]
---

# ALERT vs llm-attacks

*GraphCanon updated Aug 9, 2026*

## Verdict

Pick ALERT if aLERT is designed specifically for red-teaming based safety evaluation on large language models, using MIT licensed prompts and adversarial augmentation; pick llm-attacks if llm-attacks: Universal and Transferable Attacks on Aligned Language Models with dependency on FastChat.

[ALERT](https://arxiv.org/abs/2404.08676) reports 59 GitHub stars, 8 forks, and 0 open issues, last pushed Sep 20, 2024. [llm-attacks](https://llm-attacks.org/) has 4.8k stars, 633 forks, and 69 open issues, last pushed Aug 2, 2024. Figures are from public GitHub metadata via [ALERT's repository](https://github.com/Babelscape/ALERT) and [llm-attacks's repository](https://github.com/llm-attacks/llm-attacks).

| | [ALERT](/tools/babelscape-alert.md) | [llm-attacks](/tools/llm-attacks-llm-attacks.md) |
| --- | --- | --- |
| Tagline | A Comprehensive Benchmark for Assessing Large Language Models' Safety Through Red Teaming | Universal and Transferable Attacks on Aligned Language Models |
| Stars | 59 | 4,756 |
| Forks | 8 | 633 |
| Open issues | 0 | 69 |
| Language | Python | Python |
| Adopt for | ALERT is designed specifically for red-teaming based safety evaluation on large language models, using MIT licensed prompts and adversarial augmentation. | llm-attacks: Universal and Transferable Attacks on Aligned Language Models with dependency on FastChat. |
| Persona | - | - |
| Runtime | - | - |
| License | Other | MIT |
| Categories | Evaluation & Observability | Evaluation & Observability, LLM Frameworks |

## Trust and health

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

| | [ALERT](/tools/babelscape-alert.md) | [llm-attacks](/tools/llm-attacks-llm-attacks.md) |
| --- | --- | --- |
| Days since push | 687d | 732d |
| Open issues (now) | 0 | 69 |
| Full report | [trust report](/tools/babelscape-alert/trust.md) | [trust report](/tools/llm-attacks-llm-attacks/trust.md) |

## Decision facts: ALERT

- **Adopt for:** ALERT is designed specifically for red-teaming based safety evaluation on large language models, using MIT licensed prompts and adversarial augmentation.

## Decision facts: llm-attacks

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

## Choose when

### Choose ALERT if…

- License: ALERT is Other, llm-attacks is MIT.
- Tags unique to ALERT: ai, artificial-intelligence, benchmark, bias-detection.
- When evaluating safety metrics of large language models through red-teaming approaches

### Choose llm-attacks if…

- License: llm-attacks is MIT, ALERT is Other.
- Tags unique to llm-attacks: alignment-testing, attacks, fastchat-dependency, language-models.
- Also covers LLM Frameworks.
- When you need to test the robustness of aligned language models specifically using attacks designed for these systems,

## When NOT to use ALERT

- If your evaluation does not require bias detection or safety assessment under adversarial conditions
- In scenarios where a broader range of model aspects beyond safety is needed, as ALERT focuses primarily on safety benchmarks

## 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.

## Common questions

### What is the difference between ALERT and llm-attacks?

ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety Through Red Teaming. llm-attacks: Universal and Transferable Attacks on Aligned Language Models. See the comparison table for live GitHub stats and shared categories.

### When should I choose ALERT over llm-attacks?

Choose ALERT over llm-attacks when License: ALERT is Other, llm-attacks is MIT; Tags unique to ALERT: ai, artificial-intelligence, benchmark, bias-detection; When evaluating safety metrics of large language models through red-teaming approaches.

### When should I choose llm-attacks over ALERT?

Choose llm-attacks over ALERT when License: llm-attacks is MIT, ALERT is Other; Tags unique to llm-attacks: alignment-testing, attacks, fastchat-dependency, language-models; Also covers LLM Frameworks; When you need to test the robustness of aligned language models specifically using attacks designed for these systems,.

### When should I avoid ALERT?

If your evaluation does not require bias detection or safety assessment under adversarial conditions In scenarios where a broader range of model aspects beyond safety is needed, as ALERT focuses primarily on safety benchmarks

### 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.

### Is ALERT or llm-attacks more popular on GitHub?

llm-attacks has more GitHub stars (4,756 vs 59). Stars measure visibility, not whether either tool fits your constraints.

### Are ALERT and llm-attacks open source?

Yes - both are open-source projects on GitHub (ALERT: Other, llm-attacks: MIT).

### Where can I find alternatives to ALERT or llm-attacks?

GraphCanon lists graph-backed alternatives at [ALERT alternatives](/tools/babelscape-alert/alternatives) and [llm-attacks alternatives](/tools/llm-attacks-llm-attacks/alternatives) ([ALERT markdown twin](/tools/babelscape-alert/alternatives.md), [llm-attacks markdown twin](/tools/llm-attacks-llm-attacks/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/babelscape-alert-vs-llm-attacks-llm-attacks.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, ALERT or llm-attacks?

ALERT: Dormant. llm-attacks: 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 ALERT and llm-attacks?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ALERT trust report](/tools/babelscape-alert/trust); [llm-attacks trust report](/tools/llm-attacks-llm-attacks/trust).

---

**Machine-readable endpoints**

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