WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-8B
WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-8B is an 8 billion parameter language model developed by WhiteRabbitNeo, extending the Llama-3.1 architecture with a 32768 token context length. This model is specifically designed and fine-tuned for offensive and defensive cybersecurity applications, focusing on identifying vulnerabilities and security analysis. It excels at topics such as open port identification, outdated software detection, misconfigurations, injection flaws, and known software vulnerabilities.
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Llama-3.1-WhiteRabbitNeo-2-8B: Cybersecurity-Focused LLM
WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-8B is an 8 billion parameter language model built upon the Llama-3.1 architecture, featuring an extended context length of 32768 tokens. Developed by WhiteRabbitNeo, this model is specifically engineered for cybersecurity applications, providing capabilities for both offensive and defensive security analysis. It is released as a public preview to assess its capabilities and societal impact.
Key Capabilities
This model is designed to assist in identifying and analyzing a wide range of cybersecurity vulnerabilities, including:
- Network Security: Identifying open ports (HTTP, FTP, SSH, SMB) and unencrypted services.
- Software & System Vulnerabilities: Detecting outdated software, default credentials, misconfigurations, and known software vulnerabilities (e.g., via NVD).
- Web Application Security: Analyzing injection flaws (SQL, command, XSS), Cross-Site Request Forgery (CSRF), insecure direct object references, and API vulnerabilities.
- Authentication & Data Security: Identifying broken authentication/session management and sensitive data exposure.
- Advanced Threats: Recognizing Denial of Service (DoS) vulnerabilities and buffer overflows.
Usage Considerations
Users are solely responsible for their use of the model and its outcomes. The model is provided "as is" with no warranties. Specific usage restrictions apply, prohibiting its use for illegal activities, military applications, harming minors, generating false information with malicious intent, or any discriminatory purposes. The model utilizes the Llama-3.1 prompt format, as demonstrated in the provided sample code for interaction.