Instructions to use Xx-Vexento-xX/security-testing-agent-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Xx-Vexento-xX/security-testing-agent-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct") model = PeftModel.from_pretrained(base_model, "Xx-Vexento-xX/security-testing-agent-lora") - Notebooks
- Google Colab
- Kaggle
Security Testing Agent โ LoRA
Defensive security testing assistant. Analyzes code for vulnerabilities and suggests fixes.
Base model: meta-llama/Meta-Llama-3.1-8B-Instruct
Load it
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct")
model = PeftModel.from_pretrained(model, "Xx-Vexento-xX/security-testing-agent-lora")
tokenizer = AutoTokenizer.from_pretrained("Xx-Vexento-xX/security-testing-agent-lora")
Training
- 15 curriculum rounds across Python, JavaScript, Java, Go, PHP, Ruby, TypeScript, Rust, Kotlin, C#
- Vulnerability types: SQL Injection, XSS, CSRF, SSRF, Command Injection, IDOR, XXE, JWT weaknesses, Path Traversal, Prototype Pollution, and more
- RAG-aware: recognizes [REFERENCE CONTEXT] blocks for external knowledge injection
Intended use
Authorized defensive security testing of your own code only. Not for unauthorized access or offensive purposes.
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Model tree for Xx-Vexento-xX/security-testing-agent-lora
Base model
meta-llama/Llama-3.1-8B Finetuned
meta-llama/Llama-3.1-8B-Instruct