Cross-Site Scripting (XSS) remains a persistent and dangerous threat in web applications. Traditional detection methods have their limits, but what if you could train your own specialized AI to sniff out these vulnerabilities with greater nuance? Welcome to the world of fine-tuning Large Language Models (LLMs) for cybersecurity! In this comprehensive guide, we'll walk you through the process of supervised fine-tuning an LLM using the incredibly efficient Unsloth library to create a potent XSS detector. Whether you're a cybersecurity enthusiast, a developer, or an AI practitioner, get ready to level up your security toolkit.
Why Fine-Tune an LLM for XSS Detection?
Generic, pre-trained LLMs are powerful, but they might not possess the specialized knowledge to accurately identify complex XSS vulnerabilities within the context of specific HTTP request patterns or generate precise, actionable insights. Fine-tuning allows us to:
- Specialize: Teach the model the specific patterns and nuances of XSS vulnerabilities.
- Improve Accuracy: Achieve higher precision and recall for XSS detection compared to general models.
- Custom Output: Train the model to provide output in a structured format (like JSON) that includes not just a vulnerability flag, but also suggested payloads and reasoning.
- Understand Your Data: Tailor the model to the types of applications and codebases you typically encounter.
By using a custom "instruction-following" dataset, we can guide the LLM to become an expert XSS analyst.
Meet Unsloth: Supercharging Your Fine-Tuning Journey
Unsloth is a game-changing open-source library that makes fine-tuning LLMs significantly faster (up to 2-5x) and more memory-efficient (reducing GPU RAM usage by up to 60%) without sacrificing performance. This makes sophisticated fine-tuning accessible even on platforms like Google Colab with free-tier GPUs.
Prerequisites: What You'll Need
- Google Colab Account: We'll be using Colab for its free GPU access. Get one at colab.research.google.com.
- Hugging Face Account: To download pre-trained models and (optionally) save your fine-tuned model. Sign up at huggingface.co.
- Hugging Face Token: Create an API token with 'write' access from your Hugging Face account settings (under "Access Tokens"). This will be needed to save your model to the Hub.
- Your Custom XSS Dataset: This is crucial! You'll need a dataset in JSONL (JSON Lines) format. Each line should be a JSON object containing:
instruction: A clear task description for the LLM.input: An object containinghttp_request_details(like method, path, headers, body, injection points) for an HTTP request.output: The desired JSON response from the LLM, indicating if it's vulnerable, providing analysis, a payload, and reasoning.
- Basic Python Knowledge: Familiarity with Python will be helpful.
Sample Custom XSS Data Set
This is just my sample dataset, you can get creative on developing a better dataset
{"id": "d4e9552f44d94826913fccbb28a92e49", "instruction": "Analyze the provided HTTP endpoint details where '%QUERY%' marks a user-controlled injection point. Determine if an XSS vulnerability exists. If so, identify the vulnerable parameter or location, suggest a representative payload that would confirm the vulnerability, and provide a justification for both the vulnerability and why the payload works. If not vulnerable, state so with a detailed reason.", "input": {"description": "Analysis of potential XSS vulnerability in POST request to path '/api/v2/users/batch_update'. Injection point marked by '%QUERY%' in location type: Unknown.", "http_request_details": {"raw_endpoint_info": "POST /api/v2/users/batch_update HTTP/1.1\\nContent-Type: application/json\\n\\n{\"updates\": [{\"userId\": \"user1\", \"new_status\": \"active\"}, {\"userId\": \"user2\", \"custom_field\": \"%QUERY%\"}]}", "method": "POST", "path": "/api/v2/users/batch_update", "headers": [], "query_parameters_template": [], "body_template": null, "injection_point_marker": "%QUERY%", "injection_location_type": "Unknown"}, "payload_applied_in_source_log": "
"}, "output": {"is_vulnerable": true, "analysis": "The application fails to properly sanitize or encode user-controlled input in JSON fields, allowing injection of crafted HTML/JS that leads to XSS.", "vulnerable_parameter_suggestion": "custom_field", "recommended_payload_for_confirmation": "", "reasoning_for_payload": "Payload uses a tab-encoded \"javascript:\" URI to bypass naive filters and trigger an XSS alert in browsers parsing injected HTML."}}
{"id": "e242243ef1434f7da43d778b42825dc4", "instruction": "Analyze the provided HTTP endpoint details where '%QUERY%' marks a user-controlled injection point. Determine if an XSS vulnerability exists. If so, identify the vulnerable parameter or location, suggest a representative payload that would confirm the vulnerability, and provide a justification for both the vulnerability and why the payload works. If not vulnerable, state so with a detailed reason.", "input": {"description": "Analysis of potential XSS vulnerability in GET request to path '/playlist'. Injection point marked by '%QUERY%' in location type: Query Parameter Value ('id').", "http_request_details": {"raw_endpoint_info": "GET /playlist?id=%QUERY% HTTP/1.1", "method": "GET", "path": "/playlist", "headers": [], "query_parameters_template": [{"name": "id", "value": "%QUERY%"}], "body_template": null, "injection_point_marker": "%QUERY%", "injection_location_type": "Query Parameter Value ('id')"}, "payload_applied_in_source_log": "admin/home.php"}, "output": {"is_vulnerable": false, "analysis": "The parameter value is a file path, likely sanitized or validated server-side to prevent directory traversal or injection; no direct XSS vector in this GET request.", "vulnerable_parameter_suggestion": "Query Parameter Value ('id')", "recommended_payload_for_confirmation": "n/a", "reasoning_for_payload": "n/a"}}
The Fine-Tuning Roadmap: Step-by-Step with Code
Let's dive into the Python script that orchestrates the fine-tuning process. This script is designed for a Google Colab environment.
Step 1: Setting Up Your Colab Environment
Before running any code:
- Select GPU Runtime: In Colab, navigate to "Runtime" → "Change runtime type" and select a GPU accelerator (e.g., T4, V100).
- Add Hugging Face Token as Secret:
- Click the "Key" icon (Secrets) on the left sidebar in Colab.
- Add a new secret named
HF_TOKEN. - Paste your Hugging Face write token as the value.
- Upload Your Dataset:
- Click the "Folder" icon (Files) on the left sidebar in Colab.
- Upload your XSS dataset file (e.g.,
xss_instruction_dataset_enhanced.jsonl) to the default/content/directory.
Step 2: Installing Unsloth and Dependencies
First, we install Unsloth and Weights & Biases (wandb) for experiment tracking (optional but recommended).
# In a Colab cell:
!pip install "unsloth[colab-new]@git+https://github.com/unslothai/unsloth.git"
!pip install "wandb" # Optional, but good for tracking experiments
Step 3: Importing Essential Libraries
Next, we import all the necessary Python libraries.
from unsloth import FastLanguageModel
import torch
import json # For handling JSON data
from datasets import load_dataset, Dataset
from huggingface_hub import login
from google.colab import userdata # For accessing Colab secrets
from trl import SFTTrainer
from transformers import TrainingArguments
import pandas as pd # For loading JSONL into a DataFrame first
Step 4: Configuration is Key
Define crucial variables for our training process. Remember to update placeholders like YOUR_DATASET_PATH_HERE and YOUR_HF_USERNAME.
max_seq_length = 2048 # Max sequence length for the model. Adjust if necessary.
dtype = None # Autodetected by Unsloth. Can be torch.float16 or torch.bfloat16.
load_in_4bit = True # Use 4-bit quantization for memory efficiency.
# Hugging Face Model Configuration
# The script uses "unsloth/DeepSeek-R1-Distill-Llama-8B" by default.
# For XSS detection, consider "unsloth/llama-3-8b-Instruct-bnb-4bit" or other instruction-tuned models.
# Smaller options for testing: "unsloth/gemma-2b-it-bnb-4bit", "unsloth/Qwen1.5-1.8B-Chat-bnb-4bit"
model_name = "unsloth/DeepSeek-R1-Distill-Llama-8B" # Or your chosen model
# Dataset Configuration
# Replace with the path to your XSS dataset in JSONL format.
dataset_path = "/content/xss_instruction_dataset_enhanced.jsonl" # <<< UPDATE THIS IF YOUR FILENAME IS DIFFERENT
# Fine-tuned Model Saving Configuration
# Replace with your Hugging Face username and desired model name.
hf_username = "YOUR_HF_USERNAME" # <<< YOUR HUGGING FACE USERNAME HERE
new_model_name_online = f"{hf_username}/XSS-Analyst-{model_name.split('/')[-1]}"
new_model_name_local = f"XSS-Analyst-{model_name.split('/')[-1]}-local"
print(f"Using model: {model_name}")
print(f"Dataset path: {dataset_path}")
print(f"Model will be saved locally as: {new_model_name_local}")
print(f"Model will be saved on Hugging Face as: {new_model_name_online}")
Choosing a Base Model (model_name): While the script defaults to unsloth/DeepSeek-R1-Distill-Llama-8B, for a task requiring precise instruction following and JSON output like XSS analysis, models like unsloth/llama-3-8b-Instruct-bnb-4bit are highly recommended. For quicker testing or if you have limited resources, consider smaller models like unsloth/gemma-2b-it-bnb-4bit or unsloth/Qwen1.5-1.8B-Chat-bnb-4bit.
Step 5: Logging into Hugging Face
This step uses the HF_TOKEN secret you configured in Colab to log into your Hugging Face account, allowing you to (optionally) push your fine-tuned model to the Hub.
try:
hf_token = userdata.get('HF_TOKEN')
login(token=hf_token)
print("Successfully logged into Hugging Face.")
except Exception as e:
print(f"Could not log into Hugging Face. Ensure 'HF_TOKEN' secret is set in Colab.")
print(f"Error: {e}")
Step 6: Loading the Pre-trained Model and Tokenizer
We load the chosen base model and its tokenizer using Unsloth's FastLanguageModel. This leverages Unsloth's optimizations, including 4-bit quantization if load_in_4bit is true.
model, tokenizer = FastLanguageModel.from_pretrained(
model_name=model_name,
max_seq_length=max_seq_length,
dtype=dtype,
load_in_4bit=load_in_4bit,
)
print(f"Model '{model_name}' and tokenizer loaded successfully.")
Step 7: Crafting the Perfect Prompts for XSS Analysis
Prompt engineering is vital for instruction fine-tuning. We define specific prompt structures for both training and inference. The goal is to teach the model to take an instruction and HTTP request details as input, and produce a structured JSON analysis as output.
# This function formats the http_request_details from your dataset into a readable string.
def format_http_request_details(details_dict):
if not isinstance(details_dict, dict):
return str(details_dict) # Fallback if not a dict
parts = []
if 'raw_endpoint_info' in details_dict: # Prioritize raw_endpoint_info if available
parts.append(f"Raw HTTP Request Snippet:\n{details_dict['raw_endpoint_info']}")
else: # Fallback to structured details
if 'method' in details_dict: parts.append(f"Method: {details_dict['method']}")
if 'path' in details_dict: parts.append(f"Path: {details_dict['path']}")
if 'headers' in details_dict and details_dict['headers']:
parts.append(f"Headers: {json.dumps(details_dict['headers'])}")
if 'query_parameters_template' in details_dict and details_dict['query_parameters_template']:
params = {p['name']: p['value'] for p in details_dict['query_parameters_template'] if isinstance(p, dict)}
parts.append(f"Query Parameters: {json.dumps(params)}")
if 'body_template' in details_dict and details_dict['body_template']:
parts.append(f"Body Template: {json.dumps(details_dict['body_template'])}")
return "\n".join(parts)
# Prompt style for INFERENCE (testing the model)
inference_prompt_style = """Instruction:
{}
Input HTTP Request:
{}
Response JSON:
"""
# Prompt style for TRAINING
# The EOS_TOKEN is crucial to signal the end of a sequence.
EOS_TOKEN = tokenizer.eos_token
training_prompt_style = """<h3>Instruction:</h3>
{}
Input HTTP Request:
{}
Response JSON:
{}""" + EOS_TOKEN
print("Prompt styles defined.")
Step 8: Pre-Training Sanity Check (Optional but Smart)
Before investing time in fine-tuning, it's wise to test how the base (non-fine-tuned) model responds to a sample XSS analysis task. This gives you a baseline.
print("\nTesting model BEFORE fine-tuning...")
FastLanguageModel.for_inference(model) # Prepare model for inference
test_instruction = "Analyze the provided HTTP endpoint details where '%QUERY%' marks a user-controlled injection point. Determine if an XSS vulnerability exists. If so, identify the vulnerable parameter or location, suggest a representative payload that would confirm the vulnerability, and provide a justification for both the vulnerability and why the payload works. If not vulnerable, state so with a detailed reason. Your entire response MUST be a single, valid JSON object."
test_http_input_details = {
"raw_endpoint_info": "GET /search?query=%QUERY%&category=books HTTP/1.1\nHost: example.com",
"method": "GET",
"path": "/search",
"query_parameters_template": [{"name": "query", "value": "%QUERY%"}, {"name": "category", "value": "books"}],
"injection_point_marker": "%QUERY%",
"injection_location_type": "Query Parameter Value ('query')"
}
formatted_test_input_http = format_http_request_details(test_http_input_details)
prompt_for_pre_test = inference_prompt_style.format(test_instruction, formatted_test_input_http)
inputs = tokenizer([prompt_for_pre_test], return_tensors="pt").to("cuda")
try:
outputs = model.generate(**inputs, max_new_tokens=512, use_cache=True)
decoded_output = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
response_part = decoded_output.split("### Response JSON:")[-1].strip()
print(f"Pre-tuning Model Response:\n{response_part}")
except Exception as e:
print(f"Error during pre-tuning test: {e}")
Step 9: Initializing PEFT with LoRA
Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA (Low-Rank Adaptation) allow us to fine-tune massive LLMs with significantly fewer computational resources. We adapt only a small number of additional parameters (the LoRA adapters) instead of retraining the entire model.
model = FastLanguageModel.get_peft_model(
model,
r=16, # LoRA rank. Common values are 8, 16, 32, 64.
target_modules=[
"q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",
], # Modules to apply LoRA to. These are common for Llama-like models.
# Unsloth might auto-detect these for supported models.
lora_alpha=16, # LoRA alpha, often same as r.
lora_dropout=0, # Dropout for LoRA layers. 0 means no dropout.
bias="none", # Bias type. "none" is common.
use_gradient_checkpointing="unsloth", # Recommended by Unsloth
random_state=3407, # For reproducibility
use_rslora=False, # Rank-Stabilized LoRA
loftq_config=None, # LoftQ configuration
)
print("PEFT model initialized successfully.")
Step 10: Formatting Your Dataset for Training
The formatting_prompts_func is a crucial helper. It takes examples from your dataset and transforms them into the training_prompt_style we defined, preparing the text that the model will actually see during training.
def formatting_prompts_func(examples):
instructions = examples["instruction"]
inputs_col = examples["input"] # This is a list of dicts
outputs_col = examples["output"] # This is a list of dicts
texts = []
for instruction, input_data, output_data in zip(instructions, inputs_col, outputs_col):
# Ensure input_data and output_data are dicts, not strings (if already parsed)
if isinstance(input_data, str): input_data = json.loads(input_data)
if isinstance(output_data, str): output_data = json.loads(output_data)
http_details_str = format_http_request_details(input_data.get("http_request_details", {}))
output_json_str = json.dumps(output_data, ensure_ascii=False) # Target output
# Construct the training prompt
text = training_prompt_style.format(instruction, http_details_str, output_json_str)
texts.append(text)
return {"text": texts}
print("Dataset formatting function defined.")
Step 11: Loading and Preparing Your XSS Dataset
Here, we load your custom XSS dataset (which should be in JSONL format) using Hugging Face's datasets library and then apply the formatting_prompts_func to prepare it for the trainer.
try:
print(f"Loading dataset from: {dataset_path}")
# Ensure your dataset_path is correct and the file is uploaded to Colab.
raw_dataset = load_dataset("json", data_files={"train": dataset_path}, split="train")
# Optional: For quick testing, you might want to use a smaller subset of your data:
# raw_dataset = raw_dataset.select(range(100)) # Example: use first 100 samples
print(f"Raw dataset loaded. Number of examples: {len(raw_dataset)}")
if len(raw_dataset) > 0:
print("Example raw entry:", raw_dataset[0])
# Apply the formatting function
dataset = raw_dataset.map(formatting_prompts_func, batched=True)
print("Dataset formatted successfully.")
if len(dataset) > 0:
print("Example formatted entry (text field):", dataset[0]["text"])
except FileNotFoundError:
print(f"ERROR: Dataset file not found at {dataset_path}. Please upload your dataset and update the path in Step 4.")
raise
except Exception as e:
print(f"An error occurred during dataset loading or processing: {e}")
print("Please ensure your dataset is a valid JSONL file with 'instruction', 'input', and 'output' fields in each line.")
print("The 'input' field should be a dictionary containing 'http_request_details'.")
print("The 'output' field should be a dictionary representing the target JSON.")
raise
Step 12: Setting Up the SFTTrainer
The SFTTrainer from the TRL (Transformer Reinforcement Learning) library handles the complexities of the training loop. We configure it with our model, tokenizer, dataset, and various TrainingArguments that control aspects like batch size, learning rate, and number of epochs.
trainer = SFTTrainer(
model=model,
tokenizer=tokenizer,
train_dataset=dataset,
dataset_text_field="text", # Field in the dataset that contains the formatted prompts
max_seq_length=max_seq_length,
dataset_num_proc=2, # Number of processes for dataset mapping
packing=False, # Optional: packs multiple short sequences into one for efficiency
args=TrainingArguments(
per_device_train_batch_size=2, # Batch size per GPU
gradient_accumulation_steps=4, # Accumulate gradients over 4 steps (effective batch size = 2*4=8)
warmup_steps=10, # Number of warmup steps for the learning rate scheduler
# max_steps=60, # For quick testing. Comment out for full training.
num_train_epochs=1, # Number of training epochs. Adjust for full training (e.g., 1-3).
learning_rate=2e-4, # Learning rate
fp16=not torch.cuda.is_bf16_supported(), # Use fp16 if bf16 is not supported
bf16=torch.cuda.is_bf16_supported(), # Use bf16 if supported (newer GPUs)
logging_steps=10, # Log training metrics every 10 steps
optim="adamw_8bit", # Optimizer. adamw_8bit is memory-efficient.
weight_decay=0.01, # Weight decay
lr_scheduler_type="linear", # Learning rate scheduler type
seed=3407, # Random seed for reproducibility
output_dir="outputs", # Directory to save checkpoints and logs
report_to="wandb" if "wandb" in globals() else "none", # Report to WandB if installed, else none
),
)
print("SFTTrainer initialized.")
Important Training Arguments:
num_train_epochs: For a full run, you might set this to 1, 2, or 3 depending on your dataset size and how quickly the model learns. Start with 1.learning_rate:2e-4is a common starting point for LoRA.per_device_train_batch_sizeandgradient_accumulation_steps: These together determine your effective batch size. Adjust based on GPU memory.
Step 13: Showtime! Training the Model
This is where the magic happens! The trainer.train() call kicks off the fine-tuning process. This can take a while, from minutes for small tests to hours or even days for large datasets and many epochs.
print("\nStarting model training...")
try:
trainer_stats = trainer.train()
print("Training completed.")
print("Trainer stats:", trainer_stats)
except Exception as e:
print(f"An error occurred during training: {e}")
raise
Step 14: The Moment of Truth - Testing After Fine-Tuning
Once training is complete, we test our newly fine-tuned model on the same sample XSS scenario from Step 8. We're looking for a more accurate and well-formatted JSON response.
print("\nTesting model AFTER fine-tuning...")
FastLanguageModel.for_inference(model) # Prepare model for inference again
inputs_after_tune = tokenizer([prompt_for_pre_test], return_tensors="pt").to("cuda")
try:
outputs_after_tune = model.generate(**inputs_after_tune, max_new_tokens=512, use_cache=True)
decoded_output_after_tune = tokenizer.batch_decode(outputs_after_tune, skip_special_tokens=True)[0]
response_part_after_tune = decoded_output_after_tune.split("### Response JSON:")[-1].strip()
print(f"Fine-tuned Model Response:\n{response_part_after_tune}")
# Try to parse the JSON output
try:
parsed_json = json.loads(response_part_after_tune)
print("Successfully parsed fine-tuned model's JSON output:")
print(json.dumps(parsed_json, indent=2))
except json.JSONDecodeError as je:
print(f"Could not parse JSON from fine-tuned model's output: {je}")
print("This might indicate the model is not yet generating perfect JSON.")
except Exception as e:
print(f"Error during post-tuning test: {e}")
Step 15: Saving Your Trained Model Locally
After successful training and testing, save your fine-tuned LoRA adapters and the tokenizer locally in your Colab environment. This creates a directory named after your new_model_name_local variable.
print(f"\nSaving fine-tuned LoRA model locally to: {new_model_name_local}")
model.save_pretrained(new_model_name_local)
tokenizer.save_pretrained(new_model_name_local)
print("Model and tokenizer saved locally.")
To download these files from Colab, you'll typically zip the directory first:
In a new Colab cell, run: !zip -r {new_model_name_local}.zip {new_model_name_local} (replace {new_model_name_local} with the actual variable value if you're running this manually). Then, find the generated .zip file in the Colab file browser (left sidebar) and right-click to download.
Step 16: Sharing Your Creation - Pushing to Hugging Face Hub (Optional)
If you've configured your hf_username and logged in successfully (Step 5), you can push your LoRA adapters to the Hugging Face Hub. This makes them easily accessible from anywhere.
if hf_username != "YOUR_HF_USERNAME" and hf_token is not None: # Check token too
try:
print(f"Pushing model to Hugging Face Hub: {new_model_name_online}")
model.push_to_hub(new_model_name_online, token=hf_token)
tokenizer.push_to_hub(new_model_name_online, token=hf_token)
print("Model and tokenizer pushed to Hugging Face Hub successfully.")
except Exception as e:
print(f"Error pushing model to Hugging Face Hub: {e}")
print("Ensure your HF token has 'write' permissions and the repository name is valid.")
else:
print("\nSkipping push to Hugging Face Hub: 'YOUR_HF_USERNAME' placeholder not changed or HF token not available.")
Step 17: Next Steps - Merging and GGUF (Optional)
For some deployment scenarios, you might want to merge the LoRA adapters into the base model to create a full model checkpoint or convert it to GGUF format for use with tools like llama.cpp. Unsloth provides utilities for this (check their documentation for the latest methods).
# Example (consult Unsloth docs for current best practices):
# To merge LoRA adapters and save a full model (requires more disk space):
# model.save_pretrained_merged("merged_model_directory", tokenizer, save_method="merged_16bit") # or "merged_4bit"
# To save in GGUF format for llama.cpp:
# model.save_pretrained_gguf("gguf_model_name", tokenizer, quantization_method="q4_k_m") # Example quantization
print("\nFine-tuning script finished.")
How to Use Your Fine-Tuned XSS Hunter
Once your model is saved (either locally or on the Hugging Face Hub), you can load it for inference:
from unsloth import FastLanguageModel
# Load from local directory (where you saved LoRA adapters)
# model, tokenizer = FastLanguageModel.from_pretrained(
# model_name = "XSS-Analyst-Llama-3-8B-Instruct-local", # Or your new_model_name_local
# )
# Or load from Hugging Face Hub (if you pushed it)
# model, tokenizer = FastLanguageModel.from_pretrained(
# model_name = "YOUR_HF_USERNAME/XSS-Analyst-Llama-3-8B-Instruct", # Or your new_model_name_online
# )
# Example inference (assuming model and tokenizer are loaded):
# FastLanguageModel.for_inference(model)
#
# instruction = "Analyze the provided HTTP endpoint details..." # Your standard instruction
# http_input = { ... your http request details ... } # The HTTP request to analyze
# formatted_http = format_http_request_details(http_input) # Use the same helper
#
# prompt = inference_prompt_style.format(instruction, formatted_http)
# inputs = tokenizer([prompt], return_tensors = "pt").to("cuda")
# outputs = model.generate(**inputs, max_new_tokens = 512, use_cache = True)
# response = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
# xss_analysis_json = response.split("### Response JSON:")[-1].strip()
# print(xss_analysis_json)
Tips for Success in Fine-Tuning
- Dataset Quality is Paramount: The performance of your fine-tuned model heavily depends on the quality and diversity of your training data. Include a good mix of:
- Vulnerable (positive) examples with various XSS types.
- Non-vulnerable (negative) examples that look similar but are safe.
- Diverse HTTP methods (GET, POST, PUT, etc.), headers, and body structures.
- Varied injection points.
- Experiment with Hyperparameters: Don't be afraid to try different learning rates, batch sizes, or numbers of epochs. What works best can vary.
- Start Small, Iterate: Before running a full training job, test your pipeline with a small subset of your data (e.g., 100-500 samples) and fewer steps (e.g., `max_steps=60` in `TrainingArguments`) to catch errors quickly.
- Monitor Training: If you enable Weights & Biases (
report_to="wandb"), use its dashboard to monitor training loss and other metrics. This can provide valuable insights. - Evaluate Rigorously: Beyond the simple test in the script, plan how you will systematically evaluate your model's performance on a separate test set.
Conclusion: Your AI-Powered XSS Ally
Congratulations! You've walked through the process of supervised fine-tuning a Large Language Model for XSS detection using Unsloth. This powerful technique allows you to create specialized AI tools that can significantly enhance your web application security testing capabilities. The journey of fine-tuning is iterative; keep refining your dataset, experimenting with parameters, and exploring different base models to push the boundaries of AI in cybersecurity.
We encourage you to adapt this guide, experiment with your own XSS datasets, and share your findings with the community. The future of AI-driven security is bright, and you're now equipped to be a part of it!
Happy Fine-Tuning!
Keywords: XSS, Cross-Site Scripting, LLM, Large Language Model, Fine-Tuning, Supervised Fine-Tuning, Unsloth, Llama 3, Gemma, Qwen, AI, Artificial Intelligence, Cybersecurity, Web Security, Application Security, Vulnerability Detection, Python, Hugging Face, Google Colab, PEFT, LoRA.
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