library_name: transformers
tags:
- code
- cybersecurity
- vulnerability
- cpp
license: apache-2.0
datasets:
- lemon42-ai/minified-diverseful-multilabels
metrics:
- accuracy
base_model:
- answerdotai/ModernBERT-base
pipeline_tag: text-classification
Model Card for Model ID
This is derivative version of answerdotai/ModernBERT-base.
We fine-tuned ModernBERT-base to detect vulnerability in C/C++ Code.
The actual version has an accuracy of 82%
Model Details
Model Description
ThreatDetect-C-Cpp can be used as a code classifier. It classify the input code into 7 labels: 'safe' (no vulnerability detected) and six other CWE weaknesses:
| Label | Description |
|---|---|
| CWE-119 | Improper Restriction of Operations within the Bounds of a Memory Buffer |
| CWE-125 | Out-of-bounds Read |
| CWE-20 | Improper Input Validation |
| CWE-416 | Use After Free |
| CWE-703 | Improper Check or Handling of Exceptional Conditions |
| CWE-787 | Out-of-bounds Write |
| safe | Safe code |
- Developed by: lemon42-ai
- Contributers Abdellah Oumida & Mohamed Sbaihi
- Model type: ModernBERT, Encoder-only Transformer
- Supported Programming Languages: C/C++
- License: Apache 2.0 (see original License of ModernBERT-Base)
- Finetuned from model: answerdotai/ModernBERT-base.
Model Sources [optional]
- Repository: The official lemon42-ai Github repository
- Technical Blog Post: Coming soon.
Uses
ThreadDetect-C-Cpp can be integrated in code-related applications. For example, it can be used in pair with a code generator to detect vulnerabilities in the generated code.
Bias, Risks, and Limitations
[More Information Needed]
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]