Comparison
awesome-llm-security vs LLMs-Finetuning-Safety
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 LLMs-Finetuning-Safety if lLMs-Finetuning-Safety demonstrates the safety risks associated with fine-tuning GPT-3.5 Turbo using few adversarially designed examples.
Markdown twin · awesome-llm-security alternatives · LLMs-Finetuning-Safety alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | awesome-llm-security | LLMs-Finetuning-Safety |
|---|---|---|
| Maintenance | Slowing (351d since push) As of 2w · github_public_v1 | Dormant (893d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · 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
- LLMs-Finetuning-Safety
- Demonstrates safety risks in fine-tuning GPT-3.5 Turbo with adversarial examples
Stars
- awesome-llm-security
- 1.7k
- LLMs-Finetuning-Safety
- 358
Forks
- awesome-llm-security
- 312
- LLMs-Finetuning-Safety
- 38
Open issues
- awesome-llm-security
- 173
- LLMs-Finetuning-Safety
- 3
Language
- awesome-llm-security
- -
- LLMs-Finetuning-Safety
- 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
- LLMs-Finetuning-Safety
- LLMs-Finetuning-Safety demonstrates the safety risks associated with fine-tuning GPT-3.5 Turbo using few adversarially designed examples.
Persona
- awesome-llm-security
- -
- LLMs-Finetuning-Safety
- -
Runtime
- awesome-llm-security
- -
- LLMs-Finetuning-Safety
- -
License
- awesome-llm-security
- -
- LLMs-Finetuning-Safety
- MIT
Last pushed
- awesome-llm-security
- Aug 20, 2025
- LLMs-Finetuning-Safety
- Feb 23, 2024
Categories
- awesome-llm-security
- Evaluation & Observability
- LLMs-Finetuning-Safety
- Evaluation & Observability, Model Training
Trust and health
Maintenance
- awesome-llm-security
- Slowing (36%)
- LLMs-Finetuning-Safety
- Dormant (18%)
Days since push
- awesome-llm-security
- 351d
- LLMs-Finetuning-Safety
- 893d
Open issues (now)
- awesome-llm-security
- 173
- LLMs-Finetuning-Safety
- 3
Owner type
- awesome-llm-security
- Organization
- LLMs-Finetuning-Safety
- User
Full report
- awesome-llm-security
- Trust report
- LLMs-Finetuning-Safety
- 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, 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 LLMs-Finetuning-Safety if…
- Pricing: Open-source under the MIT license; free to use and modify. OpenAI API usage cost applies, but this repository demonstrates effects at less than $0.20..
- Tags unique to LLMs-Finetuning-Safety: adversarial training, alignment, llm-finetuning, model safety.
- Also covers Model Training.
- When evaluating the risk of compromised safety in language models after fine-tuning them on small, carefully crafted datasets.
When NOT to use LLMs-Finetuning-Safety
- When generalizing safety risks to other large language models that have different underlying architectures or safeguard mechanisms than GPT-3.5 Turbo.
- If intending to use this tool as a method of fine-tuning any model for enhancing its performance on specific tasks, given it is designed for illustrating risk rather than improving capabilities.
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-Tuning-Safety/LLMs-Finetuning-Safety) · observed Aug 5, 2026
- GitHub forks (LLM-Tuning-Safety/LLMs-Finetuning-Safety) · observed Aug 5, 2026
- Last push (LLM-Tuning-Safety/LLMs-Finetuning-Safety) · observed Feb 23, 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 · LLMs-Finetuning-Safety 358 (synced Aug 6, 2026).
Common questions
- What is the difference between awesome-llm-security and LLMs-Finetuning-Safety?
- awesome-llm-security: A curation of tools, documents and projects about LLM Security. LLMs-Finetuning-Safety: Demonstrates safety risks in fine-tuning GPT-3.5 Turbo with adversarial examples. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-llm-security over LLMs-Finetuning-Safety?
- Choose awesome-llm-security over LLMs-Finetuning-Safety 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, 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 LLMs-Finetuning-Safety over awesome-llm-security?
- Choose LLMs-Finetuning-Safety over awesome-llm-security when Pricing: Open-source under the MIT license; free to use and modify. OpenAI API usage cost applies, but this repository demonstrates effects at less than $0.20.; Tags unique to LLMs-Finetuning-Safety: adversarial training, alignment, llm-finetuning, model safety; Also covers Model Training; When evaluating the risk of compromised safety in language models after fine-tuning them on small, carefully crafted datasets.
- 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 LLMs-Finetuning-Safety?
- When generalizing safety risks to other large language models that have different underlying architectures or safeguard mechanisms than GPT-3.5 Turbo. If intending to use this tool as a method of fine-tuning any model for enhancing its performance on specific tasks, given it is designed for illustrating risk rather than improving capabilities.
- Is awesome-llm-security or LLMs-Finetuning-Safety more popular on GitHub?
- awesome-llm-security has more GitHub stars (1,672 vs 358). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-llm-security and LLMs-Finetuning-Safety open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to awesome-llm-security or LLMs-Finetuning-Safety?
- GraphCanon lists graph-backed alternatives at awesome-llm-security alternatives and LLMs-Finetuning-Safety alternatives (awesome-llm-security markdown twin, LLMs-Finetuning-Safety 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 LLMs-Finetuning-Safety?
- awesome-llm-security: Slowing. LLMs-Finetuning-Safety: 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 LLMs-Finetuning-Safety?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-llm-security trust report; LLMs-Finetuning-Safety trust report.