GraphCanon
Home/Compare/llm-attacks vs embedguard

Comparison

llm-attacks vs embedguard

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.

Markdown twin · llm-attacks alternatives · embedguard alternatives

GraphCanon updated Oct 4, 2026

7views this month

llm-attacks logo

llm-attacks

llm-attacks/llm-attacks

★ 4.8kpushed Aug 2, 2024
vs
embedguard logo

embedguard

neerazz/embedguard

★ 0pushed Aug 18, 2026

Trust & integrity

Signalllm-attacksembedguard
Maintenance
Dormant (792d since push)
As of Oct 4, 2026 · github_public_v1
Steady (30d since push)
As of Sep 18, 2026 · github_public_v1
Provenance
Not a fork · Organization account
As of Oct 4, 2026 · github_public_v1
Not a fork · Personal account
As of Sep 18, 2026 · github_public_v1
OSV dependency advisories
Published findings
As of Jul 11, 2026 · osv@v1
Published findings
As of Sep 18, 2026 · 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 reportembedguard trust reportTrust methodology

Tagline

llm-attacks
Universal and Transferable Attacks on Aligned Language Models
embedguard
Cross-Layer Detection and Provenance Attestation for Adversarial Embedding Attacks in RAG Systems

Stars

llm-attacks
4.8k
embedguard
0

Forks

llm-attacks
641
embedguard
0

Open issues

llm-attacks
69
embedguard
0

Language

llm-attacks
Python
embedguard
Python

Adopt for

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

Persona

llm-attacks
-
embedguard
-

Runtime

llm-attacks
-
embedguard
-

License

llm-attacks
MIT
embedguard
MIT License

Last pushed

llm-attacks
Aug 2, 2024
embedguard
Aug 18, 2026

Categories

llm-attacks
Evaluation & Observability, LLM Frameworks
embedguard
Evaluation & Observability, LLM Frameworks, Vector Databases

Trust and health

Maintenance

llm-attacks
Dormant (18%)
embedguard
Steady (60%)

Days since push

llm-attacks
792d
embedguard
30d

Open issues (now)

llm-attacks
69
embedguard
0

Stars delta

llm-attacks
+58 (30d)
embedguard
0 (30d)

Owner type

llm-attacks
Organization
embedguard
User

Full report

llm-attacks
Trust report
embedguard
Trust report

Shared compatibility

  • Python · llm-attacks: Python runtime · embedguard: Python runtime

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.

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 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 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.
GitHubDetails

Explore

llm-attacks trust report →embedguard trust report →Evaluation & Observability category →LLM Frameworks category →Vector Databases 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 · embedguard 0 (synced Oct 4, 2026).

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 and embedguard alternatives (llm-attacks markdown twin, embedguard 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 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; embedguard trust report.

Was this helpful?

Anonymous feedback helps us improve pages and translations.