🛡️ Falln87/Hacker-ONE 🛡️
The Premier Defensive Security Assistant for Code Analysis, Threat Hunting, & Vulnerability Research
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---
## 📖 Model Description
**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.
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.
### 🧠 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.
---
## 🚀 Getting Started
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`.*
### Installation
```bash
pip install transformers torch accelerate
```
### Quick Inference Snippet
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "Falln87/Hacker-ONE"
# Load tokenizer and model with GLM-specific configurations
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
# 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);
"
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))
```
---
## 🎯 Intended Uses & Limitations
### ✅ 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.
### 🚫 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.
### ⚠️ 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.
---
## 📊 Training Data & Methodology
Hacker-ONE was fine-tuned on a proprietary, sanitized dataset of security-specific documents. The dataset heavily prioritizes defensive remediation.
| 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. |
---
## 📈 Evaluation & Performance
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.
| 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% |
---
## ⚖️ Ethical Considerations & Compliance
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.
