Reactivating Alignment: Defending LLMs from Jailbreaks Via Intention-Aware Input-Output Matching

Large language models LLMs remain vulnerable to jailbreak attacks that conceal harmful intent within complex adversarial prompts. Existing defenses primarily rely on input perturbation or harmful-output suppression, but they rarely model where malicious intent resides, resulting in brittle...

CASCADE against Jailbreaks: Combination across Stages with Controlled Attack-Defense Evaluation

Defenses against jailbreak attacks on Large Language Models LLMs operate at different pipeline stages, such as input modification or output guard, but it remains unclear which defenses to deploy at each stage and how to combine them. Prior empirical studies, fragmented by inconsistent...

AlcaTRAz - Anchored Tree-Rule Defense against Jailbreaks

Large language models LLMs are vulnerable to jailbreak attacks that bypass safety alignment through carefully crafted prompts. Many existing defenses require access to model weights or internals, making them difficult to apply to black-box deployments. We propose AlcaTRAz Anchored Tree-Rule defen...

SkillShield: Prompt-Space Security Skills for LLM Coding Agents

A coding agent edits files and executes shell commands with its developer's privileges, allowing malicious requests to translate directly into harmful actions or functional malware. Existing defenses have complementary limitations: weight-level alignment is unavailable to API-only deployers,...

KG-DF: A Black-Box Defense Framework against Jailbreak Attacks Based on Knowledge Graphs

With the widespread application of large language models LLMs in various fields, the security challenges they face have become increasingly prominent, especially the issue of jailbreak. These attacks induce the model to generate erroneous or uncontrolled outputs through crafted inputs, threatenin...

Retrieval-Augmented Defense: Adaptive and Controllable Jailbreak Prevention for Large Language Models

Large Language Models LLMs remain vulnerable to jailbreak attacks, which attempt to elicit harmful responses from LLMs. The evolving nature and diversity of these attacks pose many challenges for defense systems, including 1 adaptation to counter emerging attack strategies without costly...

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...

One Trigger Token Is Enough: a Defense Strategy for Balancing Safety and Usability in Large Language Models

Large Language Models LLMs have been extensively used across diverse domains, including virtual assistants, automated code generation, and scientific research. However, they remain vulnerable to jailbreak attacks, which manipulate the models into generating harmful responses despite safety...