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

# llm-attacks vs embedguard

*GraphCanon updated Oct 4, 2026*

## Verdict

Pick llm-attacks if llm-attacks: Universal and Transferable Attacks on Aligned Language Models with dependency on FastChat; pick embedguard if embedguard is a Python tool designed for detecting and attesting provenance against adversarial embedding attacks in RAG systems, emphasizing AI safety and security.

[llm-attacks](https://llm-attacks.org/) reports 4.8k GitHub stars, 641 forks, and 69 open issues, last pushed Aug 2, 2024. [embedguard](https://github.com/neerazz/embedguard) has 0 stars, 0 forks, and 0 open issues, last pushed Aug 18, 2026. Figures are from public GitHub metadata via [llm-attacks's repository](https://github.com/llm-attacks/llm-attacks) and [embedguard's repository](https://github.com/neerazz/embedguard).

| | [llm-attacks](/tools/llm-attacks-llm-attacks.md) | [embedguard](/tools/neerazz-embedguard.md) |
| --- | --- | --- |
| Tagline | Universal and Transferable Attacks on Aligned Language Models | Cross-Layer Detection and Provenance Attestation for Adversarial Embedding Attacks in RAG Systems |
| Stars | 4,814 | 0 |
| Forks | 641 | 0 |
| Open issues | 69 | 0 |
| Language | Python | Python |
| Adopt for | llm-attacks: Universal and Transferable Attacks on Aligned Language Models with dependency on FastChat. | Embedguard is a Python tool designed for detecting and attesting provenance against adversarial embedding attacks in RAG systems, emphasizing AI safety and security. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | MIT License |
| Categories | Evaluation & Observability, LLM Frameworks | Evaluation & Observability, LLM Frameworks, Vector Databases |

## Trust and health

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

| | [llm-attacks](/tools/llm-attacks-llm-attacks.md) | [embedguard](/tools/neerazz-embedguard.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Steady (60%) |
| Days since push | 792d | 30d |
| Open issues (now) | 69 | 0 |
| Stars delta | +58 (30d) | 0 (30d) |
| Owner type | Organization | User |
| Full report | [trust report](/tools/llm-attacks-llm-attacks/trust.md) | [trust report](/tools/neerazz-embedguard/trust.md) |

## Shared compatibility

- **Python**: [llm-attacks](/tools/llm-attacks-llm-attacks.md) - Python runtime; [embedguard](/tools/neerazz-embedguard.md) - Python runtime

## Decision facts: llm-attacks

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

## Decision facts: embedguard

- **Requirements:** Installation requires Python and pip.; Development mode installation is supported.
- **Adopt for:** Embedguard is a Python tool designed for detecting and attesting provenance against adversarial embedding attacks in RAG systems, emphasizing AI safety and security.
- **License detail:** MIT License

## 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 0) - visibility, not fit.

### Choose embedguard if…

- Requirements: Installation requires Python and pip.; Development mode installation is supported..
- Tags unique to embedguard: ai-safety, embedding-attacks, llm, llm-security.
- Also covers Vector Databases.
- embedguard ships Docker support for self-hosted deployment.
- When you need to ensure the integrity of embeddings in RAG systems against adversarial attacks.

## 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 embedguard

- If your focus is on general-purpose AI model evaluation without a specific need for adversarial embedding attack detection.
- When your application does not involve RAG systems or you are not concerned with embedding security and provenance.

## Common questions

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

llm-attacks: Universal and Transferable Attacks on Aligned Language Models. embedguard: Cross-Layer Detection and Provenance Attestation for Adversarial Embedding Attacks in RAG Systems. See the comparison table for live GitHub stats and shared categories.

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

Choose llm-attacks over embedguard 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 0) - visibility, not fit.

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

Choose embedguard over llm-attacks when Requirements: Installation requires Python and pip.; Development mode installation is supported.; Tags unique to embedguard: ai-safety, embedding-attacks, llm, llm-security; Also covers Vector Databases; embedguard ships Docker support for self-hosted deployment; When you need to ensure the integrity of embeddings in RAG systems against adversarial attacks.

### 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 embedguard?

If your focus is on general-purpose AI model evaluation without a specific need for adversarial embedding attack detection. When your application does not involve RAG systems or you are not concerned with embedding security and provenance.

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

llm-attacks has more GitHub stars (4,814 vs 0). Stars measure visibility, not whether either tool fits your constraints.

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

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

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

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

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

llm-attacks: Dormant. embedguard: Steady. 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 embedguard?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-attacks trust report](/tools/llm-attacks-llm-attacks/trust); [embedguard trust report](/tools/neerazz-embedguard/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/_