FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Merged

TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:32kPublished:Mar 2, 2026License:llama3.1Architecture:Transformer Cold

FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Merged is an 8 billion parameter language model based on Meta's Llama-3.1-8B-Instruct, fine-tuned by FlorianJK. This model is specifically optimized using SecAlign++ to enhance its resistance against prompt injection attacks. It maintains general instruction-following quality while providing improved security against adversarial instructions, making it suitable for applications requiring robust prompt handling.

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Overview

This model, FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Merged, is an 8 billion parameter variant of Meta's Llama-3.1-8B-Instruct, fine-tuned by FlorianJK. Its primary distinction is its enhanced resistance to prompt injection attacks, achieved through a specialized fine-tuning process called SecAlign++.

Key Capabilities

  • Prompt Injection Resistance: Fine-tuned with SecAlign++ to make it robust against adversarial instructions and prompt injection attacks.
  • Instruction Following: Maintains general instruction-following quality comparable to the base Llama-3.1-8B-Instruct model, as indicated by AlpacaEval results.
  • Merged Adapter: The PEFT LoRA adapter weights are fully merged into the base model, simplifying deployment as it does not require external PEFT libraries for inference.
  • SecAlign++ Methodology: Incorporates self-generated responses for DPO signal and randomized injection positions for adversarial instructions during training, increasing robustness.

Good For

  • Secure Applications: Ideal for use cases where models might be exposed to user-generated prompts and require strong defenses against malicious inputs.
  • Robust AI Systems: Suitable for developers building applications that need to maintain consistent behavior even when faced with attempts to manipulate the model's instructions.
  • Direct Deployment: Ready for direct use with transformers and vLLM due to the merged adapter, streamlining integration into existing workflows.