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| <img src="https://images.unsplash.com/photo-1526374965328-7f61d4dc18c5?auto=format&fit=crop&q=80&w=1200" alt="Cyber Security Matrix Code" style="border-radius: 10px; margin-bottom: 20px; box-shadow: 0 4px 15px rgba(0,255,0,0.3);" /> |
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| <h1 style="color: #58a6ff;">🛡️ Falln87/Hacker-ONE 🛡️</h1> |
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| <strong>The Premier Defensive Security Assistant for Code Analysis, Threat Hunting, & Vulnerability Research</strong> |
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| ## 📖 Model Description |
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| **Hacker-ONE** is a highly specialized, fine-tuned language model built explicitly for the cybersecurity community. Built on the powerful **GLM-5.3** architecture and efficiently quantized to **BF8**, this model acts as a highly capable virtual Application Security (AppSec) engineer without the massive hardware overhead. |
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| Whether you are a security researcher hunting in bug bounties, a DevOps engineer securing a CI/CD pipeline, or a student learning secure coding, Hacker-ONE parses complex code snippets, system configurations, and raw technical logs to identify structural security flaws and generate actionable mitigation strategies. |
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| ### 🧠 Model Architecture & Details |
| * **Base Architecture:** GLM-5.3 (General Language Model) |
| * **Quantization:** BF8 (8-bit Brain Floating Point for highly efficient inference) |
| * **Language Support:** English, Python, JavaScript/TypeScript, C/C++, Java, Go, Bash, Rust, PHP. |
| * **Core Optimization:** Fine-tuned specifically for defensive security operations, code auditing, and log analysis. |
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| --- |
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| ## 🚀 Getting Started |
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| You can load and interact with Hacker-ONE using the Hugging Face `transformers` library. *Note: Because it is based on the GLM architecture, you must enable `trust_remote_code=True`.* |
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| ### Installation |
| ```bash |
| pip install transformers torch accelerate |
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| ``` |
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| ### Quick Inference Snippet |
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| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| import torch |
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| model_id = "Falln87/Hacker-ONE" |
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| # Load tokenizer and model with GLM-specific configurations |
| tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) |
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| # Loading the BF8 quantized model |
| model = AutoModelForCausalLM.from_pretrained( |
| model_id, |
| device_map="auto", |
| trust_remote_code=True, |
| # Ensure your environment supports FP8/BF8 data types |
| torch_dtype=torch.float8_e5m2 |
| ) |
| |
| prompt = " |
| [SYSTEM]: You are Hacker-ONE, a defensive security assistant. Review the provided code for vulnerabilities and suggest a fix. |
| [USER]: |
| $user_id = $_GET['id']; |
| $query = "SELECT * FROM users WHERE id = " . $user_id; |
| $result = $conn->query($query); |
| |
| |
| " |
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| inputs = tokenizer(prompt, return_tensors="pt").to("cuda") |
| outputs = model.generate(inputs, max_new_tokens=250) |
| print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
| |
| ``` |
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| --- |
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| ## 🎯 Intended Uses & Limitations |
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| ### ✅ Primary Use Cases |
| * **Static Application Security Testing (SAST):** Automated code review to spot potential flaws (SQLi, XSS, CSRF, IDOR) before deployment. |
| * **Ethical Bug Bounty Research:** Assisting researchers in understanding complex code paths, de-obfuscating scripts, and mapping out attack surfaces. |
| * **Log Analysis & Incident Response:** Parsing Apache/Nginx logs, AWS CloudTrail logs, or Windows Event Logs to identify indicators of compromise (IoCs). |
| * **Cybersecurity Education:** Helping students learn secure coding practices by explaining *why* a vulnerability exists and *how* to patch it. |
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| ### 🚫 Out-of-Scope Use |
| > **CRITICAL WARNING:** Hacker-ONE is strictly intended for **defensive and educational purposes**. The model has been aligned to refuse requests involving: |
| > * Generating active exploit payloads (e.g., weaponized malware, ransomware). |
| > * Providing step-by-step instructions for attacking unowned infrastructure. |
| > * Assisting in social engineering, phishing, or unauthorized credential harvesting. |
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| ### ⚠️ Limitations & Biases |
| * **False Positives/Negatives:** The model may hallucinate security flaws in secure code or miss deeply embedded zero-day vulnerabilities. |
| * **Business Logic Flaws:** While excellent at syntax-based bugs, AI struggles with complex business logic errors (e.g., flawed multi-step authentication processes) without heavy contextual prompting. |
| * **Hardware Compatibility:** Ensure your GPU architecture (e.g., Ada Lovelace, Hopper) natively supports 8-bit floating-point (BF8/FP8) operations for optimal inference speeds. |
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| --- |
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| ## 📊 Training Data & Methodology |
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| Hacker-ONE was fine-tuned on a proprietary, sanitized dataset of security-specific documents. The dataset heavily prioritizes defensive remediation. |
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| | Data Source Category | Description & Scope | |
| | :--- | :--- | |
| | **CVE Database & NVD** | Extensive training on resolved Common Vulnerabilities and Exposures, including CVSS scoring logic and official patch diffs. | |
| | **GitHub Commit History** | Hundreds of thousands of open-source commits tagged with "security fix," "patch," or "vulnerability." | |
| | **Standardized Frameworks** | Ingested guidelines from OWASP Top 10, MITRE ATT&CK, NIST, and SANS CWE. | |
| | **Bounty Write-ups** | Ethical bug bounty reports (HackerOne, Bugcrowd) focusing on the discovery and remediation phases. | |
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| --- |
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| ## 📈 Evaluation & Performance |
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| Hacker-ONE was evaluated against standard AppSec benchmarks. It leverages the robust GLM-5.3 reasoning capabilities to deliver high-tier vulnerability detection without introducing new flaws. |
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| | Benchmark | Focus Area | Hacker-ONE Score | Base Model Score | |
| | :--- | :--- | :---: | :---: | |
| | **HumanEval-Sec** | Generating secure code completions | **84.2%** | 68.1% | |
| | **OWASP-Detect** | Identifying Top 10 vulnerabilities | **91.5%** | 76.5% | |
| | **LogParse-QA** | Extracting IoCs from server logs | **81.0%** | 62.2% | |
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| --- |
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| ## ⚖️ Ethical Considerations & Compliance |
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| Hacker-ONE is designed with structural safeguards to prioritize **defensive mitigation advice** over offensive exploitation. By utilizing this model, users agree to operate strictly within the bounds of: |
| 1. **Coordinated Vulnerability Disclosure (CVD):** Reporting findings responsibly to vendors. |
| 2. **Rules of Engagement (RoE):** Only analyzing code or scanning systems for which you have explicit, written authorization. |
| 3. **Legal Compliance:** Adhering to the Computer Fraud and Abuse Act (CFAA) or applicable local/international cybersecurity laws. |
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| <br> |
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| <div align="center" style="background-color: #0d1117; padding: 15px; border-radius: 10px; border: 1px dashed #3fb950;"> |
| <i style="color: #c9d1d9;">"Defending the digital frontier, one line of code at a time."</i> |
| <br><br> |
| <img src="https://img.shields.io/badge/Stay_Safe-Stay_Legal-critical?style=for-the-badge" alt="Stay Safe" /> |
| <img src="https://img.shields.io/badge/White_Hat-Certified-white?style=for-the-badge&logo=hackthebox" alt="White Hat" /> |
| </div> |
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