Llama-3.1-FoundationAI-SecurityLLM-Reasoning-8B Technical Report

We present Foundation-Sec-8B-Reasoning, the first open-source native reasoning model for cybersecurity. Built upon our previously released Foundation-Sec-8B base model derived from Llama-3.1-8B-Base, the model is trained through a two-stage process combining supervised fine-tuning SFT and...

The Echo Chamber Multi-Turn LLM Jailbreak

The availability of Large Language Models LLMs has led to a new generation of powerful chatbots that can be developed at relatively low cost. As companies deploy these tools, security challenges need to be addressed to prevent financial loss and reputational damage. A key security challenge is...

SecurityLingua: Efficient Defense of LLM Jailbreak Attacks Via Security-Aware Prompt Compression

Large language models LLMs have achieved widespread adoption across numerous applications. However, many LLMs are vulnerable to malicious attacks even after safety alignment. These attacks typically bypass LLMs' safety guardrails by wrapping the original malicious instructions inside adversarial...

Why LLM Safety Guardrails Collapse after Fine-Tuning: a Similarity Analysis between Alignment and Fine-Tuning Datasets

Recent advancements in large language models LLMs have underscored their vulnerability to safety alignment jailbreaks, particularly when subjected to downstream fine-tuning. However, existing mitigation strategies primarily focus on reactively addressing jailbreak incidents after safety guardrail...

Various GPT services are vulnerable to two systemic jailbreaks, allows for bypass of safety guardrails

Overview Two systemic jailbreaks, affecting a number of generative AI services, were discovered. These jailbreaks can result in the bypass of safety protocols and allow an attacker to instruct the corresponding LLM to provide illicit or dangerous content. The first jailbreak, called “Inception,” ...

Researchers Reveal 'Deceptive Delight' Method to Jailbreak AI Models

Cybersecurity researchers have shed light on a new adversarial technique that could be used to jailbreak large language models LLMs during the course of an interactive conversation by sneaking in an undesirable instruction between benign ones. The approach has been codenamed Deceptive Delight by...