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Comparison

llm-guard vs plexiglass

Verdict

Pick llm-guard if lLM Guard is a toolkit tailored for securing interactions with large language models, focusing on safeguarding against prompt injection and adversarial attacks; pick plexiglass if plexiglass is a Python-based toolkit aimed at detecting and mitigating vulnerabilities in Large Language Models, with a focus on cybersecurity and adversarial attacks.

Markdown twin · llm-guard alternatives · plexiglass alternatives

GraphCanon updated Oct 6, 2026

6views this month

llm-guard logo

llm-guard

protectai/llm-guard

★ 3.2kpushed Jul 8, 2026
vs
plexiglass logo

plexiglass

w4-advisory/plexiglass

★ 156pushed Feb 4, 2026

Trust & integrity

Signalllm-guardplexiglass
Maintenance
Archived (89d since push)
As of Oct 6, 2026 · github_public_v1
Slowing (225d since push)
As of Sep 18, 2026 · github_public_v1
Provenance
Not a fork · Organization account
As of Oct 6, 2026 · github_public_v1
Not a fork · Organization account
As of Sep 18, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of Jul 11, 2026 · osv@v1
No lockfile (source not queried)
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-guard trust reportplexiglass trust reportTrust methodology

Tagline

llm-guard
The Security Toolkit for LLM Interactions
plexiglass
A toolkit for detecting and protecting against vulnerabilities in Large Language Models (LLMs).

Stars

llm-guard
3.2k
plexiglass
156

Forks

llm-guard
462
plexiglass
18

Open issues

llm-guard
38
plexiglass
0

Language

llm-guard
Python
plexiglass
Python

Adopt for

llm-guard
LLM Guard is a toolkit tailored for securing interactions with large language models, focusing on safeguarding against prompt injection and adversarial attacks.
plexiglass
Plexiglass is a Python-based toolkit aimed at detecting and mitigating vulnerabilities in Large Language Models, with a focus on cybersecurity and adversarial attacks.

Persona

llm-guard
-
plexiglass
-

Runtime

llm-guard
-
plexiglass
-

License

llm-guard
MIT
plexiglass
Plexiglass is available under the Apache-2.0 license, allowing for broad usage and modification with attribution.

Last pushed

llm-guard
Jul 8, 2026
plexiglass
Feb 4, 2026

Categories

llm-guard
Developer Tools, Evaluation & Observability
plexiglass
Evaluation & Observability

Trust and health

Maintenance

llm-guard
Archived (8%)
plexiglass
Slowing (36%)

Days since push

llm-guard
89d
plexiglass
225d

Archived on GitHub

llm-guard
Yes
plexiglass
No

Open issues (now)

llm-guard
38
plexiglass
0

Stars delta

llm-guard
+9 (30d)
plexiglass
+3 (30d)

Full report

llm-guard
Trust report
plexiglass
Trust report

Shared compatibility

  • Python · llm-guard: Python runtime · plexiglass: Python runtime

Choose llm-guard if…

  • License: llm-guard is MIT, plexiglass is Apache-2.0.
  • Requirements: Min 2 GB RAM; Ensure you have Python version 3.9 or higher.; Some advanced features require additional libraries which are automatically installed as needed..
  • Tags unique to llm-guard: chatgpt, large-language-models, llm-security, prompt-engineering.
  • Also covers Developer Tools.
  • - You need to secure your application from sophisticated prompt injection techniques.

When NOT to use llm-guard

  • - If you are working in a low-security environment or with small-scale projects where advanced security mechanisms are not necessary.
  • - In cases where integrating external libraries and ensuring Python version compatibility may introduce complexities that outweigh the benefits.
GitHubDetails

Choose plexiglass if…

  • License: plexiglass is Apache-2.0, llm-guard is MIT.
  • Pricing: The toolkit is free to use under the Apache-2.0 license, but advanced features or support might incur costs..
  • Requirements: Min 4 GB RAM; Python environment is required for installation and operation..
  • Tags unique to plexiglass: adversarial-attacks, cybersecurity, deep-learning, deep-neural-networks.
  • When you need to evaluate the robustness of Large Language Models against adversarial attacks.

When NOT to use plexiglass

  • If your project does not involve Large Language Models and you are looking for general cybersecurity tools.
  • When you are working with small-scale models where the overhead of using Plexiglass might not be justified.
GitHubDetails

Explore

llm-guard trust report →plexiglass trust report →Developer Tools category →Evaluation & Observability 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-guard 3.2k · plexiglass 156 (synced Oct 6, 2026).

Common questions

What is the difference between llm-guard and plexiglass?
llm-guard: The Security Toolkit for LLM Interactions. 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 llm-guard over plexiglass?
Choose llm-guard over plexiglass when License: llm-guard is MIT, plexiglass is Apache-2.0; Requirements: Min 2 GB RAM; Ensure you have Python version 3.9 or higher.; Some advanced features require additional libraries which are automatically installed as needed.; Tags unique to llm-guard: chatgpt, large-language-models, llm-security, prompt-engineering; Also covers Developer Tools; - You need to secure your application from sophisticated prompt injection techniques.
When should I choose plexiglass over llm-guard?
Choose plexiglass over llm-guard when License: plexiglass is Apache-2.0, llm-guard is MIT; Pricing: The toolkit is free to use under the Apache-2.0 license, but advanced features or support might incur costs.; Requirements: Min 4 GB RAM; Python environment is required for installation and operation.; Tags unique to plexiglass: adversarial-attacks, cybersecurity, deep-learning, deep-neural-networks; When you need to evaluate the robustness of Large Language Models against adversarial attacks.
When should I avoid llm-guard?
- If you are working in a low-security environment or with small-scale projects where advanced security mechanisms are not necessary. - In cases where integrating external libraries and ensuring Python version compatibility may introduce complexities that outweigh the benefits.
When should I avoid plexiglass?
If your project does not involve Large Language Models and you are looking for general cybersecurity tools. When you are working with small-scale models where the overhead of using Plexiglass might not be justified.
Is llm-guard or plexiglass more popular on GitHub?
llm-guard has more GitHub stars (3,213 vs 156). Stars measure visibility, not whether either tool fits your constraints.
Are llm-guard and plexiglass open source?
Yes - both are open-source projects on GitHub (llm-guard: MIT, plexiglass: Apache-2.0).
Where can I find alternatives to llm-guard or plexiglass?
GraphCanon lists graph-backed alternatives at llm-guard alternatives and plexiglass alternatives (llm-guard 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, llm-guard or plexiglass?
llm-guard: Archived. 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 llm-guard and plexiglass?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: llm-guard trust report; plexiglass trust report.

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