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Comparison

ALERT vs awesome-llm-security

Verdict

Pick ALERT if aLERT is designed specifically for red-teaming based safety evaluation on large language models, using MIT licensed prompts and adversarial augmentation; 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.

Markdown twin · ALERT alternatives · awesome-llm-security alternatives

GraphCanon updated Sep 10, 2026

8views this month

ALERT logo

ALERT

Babelscape/ALERT

★ 59pushed Sep 20, 2024
vs
awesome-llm-security logo

awesome-llm-security

corca-ai/awesome-llm-security

★ 1.7kpushed Aug 20, 2025

Trust & integrity

SignalALERTawesome-llm-security
Maintenance
Dormant (719d since push)
As of Sep 10, 2026 · github_public_v1
Dormant (382d since push)
As of Sep 6, 2026 · github_public_v1
Provenance
Not a fork · Organization account
As of Sep 10, 2026 · github_public_v1
Not a fork · Organization account
As of Sep 6, 2026 · github_public_v1
OSV dependency advisories
No published findings from this source as of 2026-07-15
As of Jul 15, 2026 · osv@v1
No lockfile (source not queried)
As of Jul 11, 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
ALERT trust reportawesome-llm-security trust reportTrust methodology

Tagline

ALERT
A Comprehensive Benchmark for Assessing Large Language Models' Safety Through Red Teaming
awesome-llm-security
A curation of tools, documents and projects about LLM Security

Stars

ALERT
59
awesome-llm-security
1.7k

Forks

ALERT
8
awesome-llm-security
347

Open issues

ALERT
0
awesome-llm-security
207

Language

ALERT
Python
awesome-llm-security
-

Adopt for

ALERT
ALERT is designed specifically for red-teaming based safety evaluation on large language models, using MIT licensed prompts and adversarial augmentation.
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

Persona

ALERT
-
awesome-llm-security
-

Runtime

ALERT
-
awesome-llm-security
-

License

ALERT
Other
awesome-llm-security
-

Last pushed

ALERT
Sep 20, 2024
awesome-llm-security
Aug 20, 2025

Categories

ALERT
Evaluation & Observability
awesome-llm-security
Evaluation & Observability

Trust and health

Days since push

ALERT
719d
awesome-llm-security
382d

Open issues (now)

ALERT
0
awesome-llm-security
207

Stars delta

ALERT
0 (30d)
awesome-llm-security
+20 (30d)

Open issues delta

ALERT
0 (30d)
awesome-llm-security
+34 (30d)

OSV dependency advisories

ALERT
No published findings from this source as of 2026-07-15
awesome-llm-security
No lockfile (source not queried)

Full report

ALERT
Trust report
awesome-llm-security
Trust report

Choose ALERT if…

  • Tags unique to ALERT: ai, artificial-intelligence, benchmark, bias-detection.
  • When evaluating safety metrics of large language models through red-teaming approaches
  • Leaner open-issue backlog (0).

When NOT to use ALERT

  • If your evaluation does not require bias detection or safety assessment under adversarial conditions
  • In scenarios where a broader range of model aspects beyond safety is needed, as ALERT focuses primarily on safety benchmarks
GitHubDetails

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.
GitHubDetails

Explore

ALERT trust report →awesome-llm-security trust report →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: ALERT 59 · awesome-llm-security 1.7k (synced Sep 10, 2026).

Common questions

What is the difference between ALERT and awesome-llm-security?
ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety Through Red Teaming. awesome-llm-security: A curation of tools, documents and projects about LLM Security. See the comparison table for live GitHub stats and shared categories.
When should I choose ALERT over awesome-llm-security?
Choose ALERT over awesome-llm-security when Tags unique to ALERT: ai, artificial-intelligence, benchmark, bias-detection; When evaluating safety metrics of large language models through red-teaming approaches; Leaner open-issue backlog (0).
When should I choose awesome-llm-security over ALERT?
Choose awesome-llm-security over ALERT 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 avoid ALERT?
If your evaluation does not require bias detection or safety assessment under adversarial conditions In scenarios where a broader range of model aspects beyond safety is needed, as ALERT focuses primarily on safety benchmarks
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.
Is ALERT or awesome-llm-security more popular on GitHub?
awesome-llm-security has more GitHub stars (1,692 vs 59). Stars measure visibility, not whether either tool fits your constraints.
Are ALERT and awesome-llm-security open source?
Yes - both are open-source projects on GitHub.
Where can I find alternatives to ALERT or awesome-llm-security?
GraphCanon lists graph-backed alternatives at ALERT alternatives and awesome-llm-security alternatives (ALERT markdown twin, awesome-llm-security 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, ALERT or awesome-llm-security?
ALERT: Dormant. awesome-llm-security: 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 ALERT and awesome-llm-security?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ALERT trust report; awesome-llm-security trust report.

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