Comparison
awesome-llm-security vs plexiglass
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 plexiglass if plexiglass is a toolkit for detecting and mitigating vulnerabilities in Large Language Models through adversarial attacks and deep-learning techniques.
Markdown twin · awesome-llm-security alternatives · plexiglass alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | awesome-llm-security | plexiglass |
|---|---|---|
| Maintenance | Slowing (351d since push) As of 2w · github_public_v1 | Slowing (178d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) 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
- plexiglass
- A toolkit for detecting and protecting against vulnerabilities in Large Language Models (LLMs).
Stars
- awesome-llm-security
- 1.7k
- plexiglass
- 153
Forks
- awesome-llm-security
- 312
- plexiglass
- 18
Open issues
- awesome-llm-security
- 173
- plexiglass
- 0
Language
- awesome-llm-security
- -
- plexiglass
- 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
- plexiglass
- Plexiglass is a toolkit for detecting and mitigating vulnerabilities in Large Language Models through adversarial attacks and deep-learning techniques.
Persona
- awesome-llm-security
- -
- plexiglass
- -
Runtime
- awesome-llm-security
- -
- plexiglass
- -
License
- awesome-llm-security
- -
- plexiglass
- Apache-2.0
Last pushed
- awesome-llm-security
- Aug 20, 2025
- plexiglass
- Feb 4, 2026
Categories
- awesome-llm-security
- Evaluation & Observability
- plexiglass
- Evaluation & Observability
Trust and health
Days since push
- awesome-llm-security
- 351d
- plexiglass
- 178d
Open issues (now)
- awesome-llm-security
- 173
- plexiglass
- 0
Full report
- awesome-llm-security
- Trust report
- plexiglass
- 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.
- 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 plexiglass if…
- Tags unique to plexiglass: adversarial-attacks, adversarial-machine-learning, cybersecurity, deep-learning.
- When a team needs to evaluate the robustness of their LLM against specific adversarial attack vectors within Python-based projects.
- More recently updated (last pushed Feb 4, 2026).
When NOT to use plexiglass
- If a project is not using Python, because Plexiglass is specifically built for Python environments.
- When the team does not have access to the deep-learning techniques required by Plexiglass, as it heavily relies on such methods to mitigate vulnerabilities.
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 (safellama/plexiglass) · observed Aug 2, 2026
- GitHub forks (safellama/plexiglass) · observed Aug 2, 2026
- Last push (safellama/plexiglass) · observed Feb 4, 2026
- License file (Apache-2.0) · observed Aug 2, 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 · plexiglass 153 (synced Aug 6, 2026).
Common questions
- What is the difference between awesome-llm-security and plexiglass?
- awesome-llm-security: A curation of tools, documents and projects about LLM Security. plexiglass: A toolkit for detecting and protecting against vulnerabilities in Large Language Models (LLMs).. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-llm-security over plexiglass?
- Choose awesome-llm-security over plexiglass 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; 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 plexiglass over awesome-llm-security?
- Choose plexiglass over awesome-llm-security when Tags unique to plexiglass: adversarial-attacks, adversarial-machine-learning, cybersecurity, deep-learning; When a team needs to evaluate the robustness of their LLM against specific adversarial attack vectors within Python-based projects; More recently updated (last pushed Feb 4, 2026).
- 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 plexiglass?
- If a project is not using Python, because Plexiglass is specifically built for Python environments. When the team does not have access to the deep-learning techniques required by Plexiglass, as it heavily relies on such methods to mitigate vulnerabilities.
- Is awesome-llm-security or plexiglass more popular on GitHub?
- awesome-llm-security has more GitHub stars (1,672 vs 153). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-llm-security and plexiglass open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to awesome-llm-security or plexiglass?
- GraphCanon lists graph-backed alternatives at awesome-llm-security alternatives and plexiglass alternatives (awesome-llm-security markdown twin, plexiglass 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 plexiglass?
- awesome-llm-security: Slowing. plexiglass: 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 awesome-llm-security and plexiglass?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-llm-security trust report; plexiglass trust report.