Comparison
last_layer vs Model-Fingerprint
Verdict
Pick last_layer if an ultra-fast Python tool for detecting prompt injections and jailbreak attempts in large language models suitable for projects requiring rapid security evaluations, with low-latency performance; pick Model-Fingerprint if model-Fingerprint is a toolset for creating instructional fingerprints of large language models using CUDA 11.3 and PyTorch 2.0.
Markdown twin · last_layer alternatives · Model-Fingerprint alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | last_layer | Model-Fingerprint |
|---|---|---|
| Maintenance | Dormant (744d since push) As of 2w · github_public_v1 | Dormant (754d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | No published findings from this source as of 2026-07-11 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
- last_layer
- Ultra-fast low latency LLM prompt injection jailbreak detection
- Model-Fingerprint
- Fingerprint large language models
Stars
- last_layer
- 131
- Model-Fingerprint
- 52
Forks
- last_layer
- 4
- Model-Fingerprint
- 8
Open issues
- last_layer
- 13
- Model-Fingerprint
- 5
Language
- last_layer
- Python
- Model-Fingerprint
- Python
Adopt for
- last_layer
- An ultra-fast Python tool for detecting prompt injections and jailbreak attempts in large language models suitable for projects requiring rapid security evaluations, with low-latency performance.
- Model-Fingerprint
- Model-Fingerprint is a toolset for creating instructional fingerprints of large language models using CUDA 11.3 and PyTorch 2.0.
Persona
- last_layer
- -
- Model-Fingerprint
- -
Runtime
- last_layer
- -
- Model-Fingerprint
- -
License
- last_layer
- MIT
- Model-Fingerprint
- MIT
Last pushed
- last_layer
- Jul 26, 2024
- Model-Fingerprint
- Jul 11, 2024
Categories
- last_layer
- Evaluation & Observability
- Model-Fingerprint
- Evaluation & Observability
Trust and health
Days since push
- last_layer
- 744d
- Model-Fingerprint
- 754d
Open issues (now)
- last_layer
- 13
- Model-Fingerprint
- 5
OSV dependency advisories
- last_layer
- Published findings
- Model-Fingerprint
- No published findings from this source as of 2026-07-11
Full report
- last_layer
- Trust report
- Model-Fingerprint
- Trust report
Shared compatibility
- Python · last_layer: Python runtime · Model-Fingerprint: Python runtime
Choose last_layer if…
- Tags unique to last_layer: chatgpt-prompts, jailbreak, llm security, llm-guard.
- When you need fast detection of potential security vulnerabilities due to unauthorized prompt manipulations in real-time scenarios involving LLMs
- More GitHub stars (131 vs 52) - visibility, not fit.
When NOT to use last_layer
- If your application does not require ultra-low latency detection and can afford slower, potentially more comprehensive security evaluations
- For environments that prefer a broader range of security features beyond prompt injection detection, as last_layer focuses specifically on this aspect with speed in mind
Choose Model-Fingerprint if…
- Tags unique to Model-Fingerprint: fingerprinting, pytorch.
- Use Model-Fingerprint when you need to fingerprint large language models for evaluation or observability purposes, especially in research contexts involving CUDA 11.3 and PyTorch 2.0 environments.
- Leaner open-issue backlog (5).
When NOT to use Model-Fingerprint
- Do not use Model-Fingerprint if your development environment does not support CUDA 11.3 and PyTorch 2.0, as it may lead to incompatibility issues.
- Avoid this toolset if you need a solution that supports multiple versions of CUDA or Pytorch for flexibility across different hardware configurations without modification.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (arekusandr/last_layer) · observed Aug 10, 2026
- GitHub forks (arekusandr/last_layer) · observed Aug 10, 2026
- Last push (arekusandr/last_layer) · observed Jul 26, 2024
- License file (MIT) · observed Aug 10, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (cnut1648/Model-Fingerprint) · observed Aug 5, 2026
- GitHub forks (cnut1648/Model-Fingerprint) · observed Aug 5, 2026
- Last push (cnut1648/Model-Fingerprint) · observed Jul 11, 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: last_layer 131 · Model-Fingerprint 52 (synced Aug 10, 2026).
Common questions
- What is the difference between last_layer and Model-Fingerprint?
- last_layer: Ultra-fast low latency LLM prompt injection jailbreak detection. Model-Fingerprint: Fingerprint large language models. See the comparison table for live GitHub stats and shared categories.
- When should I choose last_layer over Model-Fingerprint?
- Choose last_layer over Model-Fingerprint when Tags unique to last_layer: chatgpt-prompts, jailbreak, llm security, llm-guard; When you need fast detection of potential security vulnerabilities due to unauthorized prompt manipulations in real-time scenarios involving LLMs; More GitHub stars (131 vs 52) - visibility, not fit.
- When should I choose Model-Fingerprint over last_layer?
- Choose Model-Fingerprint over last_layer when Tags unique to Model-Fingerprint: fingerprinting, pytorch; Use Model-Fingerprint when you need to fingerprint large language models for evaluation or observability purposes, especially in research contexts involving CUDA 11.3 and PyTorch 2.0 environments; Leaner open-issue backlog (5).
- When should I avoid last_layer?
- If your application does not require ultra-low latency detection and can afford slower, potentially more comprehensive security evaluations For environments that prefer a broader range of security features beyond prompt injection detection, as last_layer focuses specifically on this aspect with speed in mind
- When should I avoid Model-Fingerprint?
- Do not use Model-Fingerprint if your development environment does not support CUDA 11.3 and PyTorch 2.0, as it may lead to incompatibility issues. Avoid this toolset if you need a solution that supports multiple versions of CUDA or Pytorch for flexibility across different hardware configurations without modification.
- Is last_layer or Model-Fingerprint more popular on GitHub?
- last_layer has more GitHub stars (131 vs 52). Stars measure visibility, not whether either tool fits your constraints.
- Are last_layer and Model-Fingerprint open source?
- Yes - both are open-source projects on GitHub (last_layer: MIT, Model-Fingerprint: MIT).
- Where can I find alternatives to last_layer or Model-Fingerprint?
- GraphCanon lists graph-backed alternatives at last_layer alternatives and Model-Fingerprint alternatives (last_layer markdown twin, Model-Fingerprint 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, last_layer or Model-Fingerprint?
- last_layer: Dormant. Model-Fingerprint: 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 last_layer and Model-Fingerprint?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: last_layer trust report; Model-Fingerprint trust report.