--- title: "last_layer vs Model-Fingerprint" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/arekusandr-last-layer-vs-cnut1648-model-fingerprint" tools: ["arekusandr-last-layer", "cnut1648-model-fingerprint"] --- # last_layer vs Model-Fingerprint *GraphCanon updated Aug 10, 2026* ## 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. [last_layer](https://vibe-eval.com) reports 131 GitHub stars, 4 forks, and 13 open issues, last pushed Jul 26, 2024. [Model-Fingerprint](https://github.com/cnut1648/Model-Fingerprint) has 52 stars, 8 forks, and 5 open issues, last pushed Jul 11, 2024. Figures are from public GitHub metadata via [last_layer's repository](https://github.com/arekusandr/last_layer) and [Model-Fingerprint's repository](https://github.com/cnut1648/Model-Fingerprint). | | [last_layer](/tools/arekusandr-last-layer.md) | [Model-Fingerprint](/tools/cnut1648-model-fingerprint.md) | | --- | --- | --- | | Tagline | Ultra-fast low latency LLM prompt injection jailbreak detection | Fingerprint large language models | | Stars | 131 | 52 | | Forks | 4 | 8 | | Open issues | 13 | 5 | | Language | Python | Python | | Adopt for | 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 is a toolset for creating instructional fingerprints of large language models using CUDA 11.3 and PyTorch 2.0. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [last_layer](/tools/arekusandr-last-layer.md) | [Model-Fingerprint](/tools/cnut1648-model-fingerprint.md) | | --- | --- | --- | | Days since push | 744d | 754d | | Open issues (now) | 13 | 5 | | Full report | [trust report](/tools/arekusandr-last-layer/trust.md) | [trust report](/tools/cnut1648-model-fingerprint/trust.md) | ## Shared compatibility - **Python**: [last_layer](/tools/arekusandr-last-layer.md) - Python runtime; [Model-Fingerprint](/tools/cnut1648-model-fingerprint.md) - Python runtime ## Decision facts: last_layer - **Adopt for:** 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. ## Decision facts: Model-Fingerprint - **Adopt for:** Model-Fingerprint is a toolset for creating instructional fingerprints of large language models using CUDA 11.3 and PyTorch 2.0. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/arekusandr-last-layer/alternatives) and [Model-Fingerprint alternatives](/tools/cnut1648-model-fingerprint/alternatives) ([last_layer markdown twin](/tools/arekusandr-last-layer/alternatives.md), [Model-Fingerprint markdown twin](/tools/cnut1648-model-fingerprint/alternatives.md)), 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](/compare/arekusandr-last-layer-vs-cnut1648-model-fingerprint.md) 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](/tools/arekusandr-last-layer/trust); [Model-Fingerprint trust report](/tools/cnut1648-model-fingerprint/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=arekusandr-last-layer`](/api/graphcanon/graph?tool=arekusandr-last-layer) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_