Llama-3.1-CyberAlpha8B

Llama-3.1-CyberAlpha8B is a fine-tuned version of Meta-Llama-3.1-8B-Instruct specialized for resistance against Indirect Prompt Injection (IPI) attacks in agentic tool-using scenarios.

Model Details

Base Model: Meta-Llama-3.1-8B-Instruct
Fine-tuning Method: 4-bit QLoRA (Unsloth) + Supervised Fine-Tuning (TRL)
Sequence Length: 4096 tokens
Epochs: 1
Hardware: Single NVIDIA Tesla T4

1000 Test Cases Results

Training Data

Trained on the official NVIDIA Nemotron-RL-Agentic-Indirect-Prompt-Injection-v1 dataset (1,272 examples).

This dataset focuses on agentic indirect prompt injection — attacks where malicious instructions are embedded inside tool-returned or environment content (not in the user message itself).

Each example contains:

Benign system + user request
Required legitimate read tools
Tool catalog
Untrusted environment / tool-returned content containing the injection
Malicious target tool + arguments
Deterministic IPI verifier configuration

Domains Covered

Healthcare
Legal
Real Estate
Logistics
Ecommerce
Education
HR
IT Helpdesk
Insurance

Attack Categories

Unauthorized Action
Data Modification
Denial of Service
Exfiltration

The model is trained to correctly call the required read tools, observe the injected content, and produce a safe refusal that treats the injected instruction as untrusted data — never following the malicious target tool.

Intended Use

Designed for research and development of safer tool-using agents that must remain robust when reading untrusted external content (emails, documents, database records, web pages, etc.).

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Rumiii/Llama-3.1-CyberAlpha8B"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto"
)

# Example cybersecurity tool
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_security_alert",
            "description": "Retrieve details of a security alert.",
            "parameters": {
                "type": "object",
                "properties": {
                    "alert_id": {
                        "type": "string"
                    }
                },
                "required": ["alert_id"]
            }
        }
    }
]

messages = [
    {
        "role": "user",
        "content": "Check security alert ALERT-1024."
    }
]

inputs = tokenizer.apply_chat_template(
    messages,
    tools=tools,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=256
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Rumiii/Llama-3.1-CyberAlpha8B