Comparison
awesome-llm-security vs llm-attacks
Verdict
Pick awesome-llm-security if awesome LLM Security is a curated list of resources related to the security aspects of large language models. It covers various attack methodologies, defenses, and platform security through papers, benchmarks, tools, and; pick llm-attacks if llm-attacks: Universal and Transferable Attacks on Aligned Language Models with dependency on FastChat.
Markdown twin · awesome-llm-security alternatives · llm-attacks alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | awesome-llm-security | llm-attacks |
|---|---|---|
| Maintenance | Slowing (351d since push) As of 2w · github_public_v1 | Dormant (732d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings 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 |
Tagline
- awesome-llm-security
- A curation of tools, documents and projects about LLM Security
- llm-attacks
- Universal and Transferable Attacks on Aligned Language Models
Stars
- awesome-llm-security
- 1.7k
- llm-attacks
- 4.8k
Forks
- awesome-llm-security
- 312
- llm-attacks
- 633
Open issues
- awesome-llm-security
- 173
- llm-attacks
- 69
Language
- awesome-llm-security
- -
- llm-attacks
- Python
Adopt for
- awesome-llm-security
- Awesome LLM Security is a curated list of resources related to the security aspects of large language models. It covers various attack methodologies, defenses, and platform security through papers, benchmarks, tools, and
- llm-attacks
- llm-attacks: Universal and Transferable Attacks on Aligned Language Models with dependency on FastChat.
Persona
- awesome-llm-security
- -
- llm-attacks
- -
Runtime
- awesome-llm-security
- -
- llm-attacks
- -
License
- awesome-llm-security
- -
- llm-attacks
- MIT
Last pushed
- awesome-llm-security
- Aug 20, 2025
- llm-attacks
- Aug 2, 2024
Categories
- awesome-llm-security
- Evaluation & Observability
- llm-attacks
- Evaluation & Observability, LLM Frameworks
Trust and health
Maintenance
- awesome-llm-security
- Slowing (36%)
- llm-attacks
- Dormant (18%)
Days since push
- awesome-llm-security
- 351d
- llm-attacks
- 732d
Open issues (now)
- awesome-llm-security
- 173
- llm-attacks
- 69
OSV dependency advisories
- awesome-llm-security
- No lockfile (source not queried)
- llm-attacks
- Published findings
Full report
- awesome-llm-security
- Trust report
- llm-attacks
- Trust report
Choose awesome-llm-security if…
- Pricing: As an open-source project without defined pricing models, its use is generally free under the terms of its license (license details are not provided)..
- Tags unique to awesome-llm-security: awesome-list, llm, security.
- When you are specifically looking for detailed information on both white-box and black-box attacks targeted at Large Language Models (LLMs), which 'awesome-llm-security' comprehensively catalogs.
When NOT to use awesome-llm-security
- When your primary interest is in general software security or vulnerabilities unrelated to language models, since 'awesome-llm-security' zeroes in on attack vectors specifically for LLMs.
- If you are solely interested in tools and methods that are not publicly discussed or peer-reviewed; the repository focuses on documented approaches within reputable academic publications.
Choose llm-attacks if…
- 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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (corca-ai/awesome-llm-security) · observed Aug 6, 2026
- GitHub forks (corca-ai/awesome-llm-security) · observed Aug 6, 2026
- Last push (corca-ai/awesome-llm-security) · observed Aug 20, 2025
- License file (unknown) · observed Aug 6, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (llm-attacks/llm-attacks) · observed Aug 5, 2026
- GitHub forks (llm-attacks/llm-attacks) · observed Aug 5, 2026
- Last push (llm-attacks/llm-attacks) · observed Aug 2, 2024
- License file (MIT) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-llm-security 1.7k · llm-attacks 4.8k (synced Aug 6, 2026).
Common questions
- What is the difference between awesome-llm-security and llm-attacks?
- awesome-llm-security: A curation of tools, documents and projects about LLM Security. 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 awesome-llm-security over llm-attacks?
- Choose awesome-llm-security over llm-attacks when Pricing: As an open-source project without defined pricing models, its use is generally free under the terms of its license (license details are not provided).; Tags unique to awesome-llm-security: awesome-list, llm, security; When you are specifically looking for detailed information on both white-box and black-box attacks targeted at Large Language Models (LLMs), which 'awesome-llm-security' comprehensively catalogs.
- When should I choose llm-attacks over awesome-llm-security?
- Choose llm-attacks over awesome-llm-security when 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 awesome-llm-security?
- When your primary interest is in general software security or vulnerabilities unrelated to language models, since 'awesome-llm-security' zeroes in on attack vectors specifically for LLMs. If you are solely interested in tools and methods that are not publicly discussed or peer-reviewed; the repository focuses on documented approaches within reputable academic publications.
- 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 awesome-llm-security or llm-attacks more popular on GitHub?
- llm-attacks has more GitHub stars (4,756 vs 1,672). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-llm-security and llm-attacks open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to awesome-llm-security or llm-attacks?
- GraphCanon lists graph-backed alternatives at awesome-llm-security alternatives and llm-attacks alternatives (awesome-llm-security markdown twin, llm-attacks 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, awesome-llm-security or llm-attacks?
- awesome-llm-security: Slowing. 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 awesome-llm-security and llm-attacks?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-llm-security trust report; llm-attacks trust report.