The paper proposes the Adversarial Prompt Disentanglement (APD) framework, a novel defense mechanism that proactively identifies and neutralizes malicious components in LLM prompts, achieving over 85%…

AttackEval systematically evaluates the effectiveness of 250 prompt injection prompts across ten attack categories, finding that composite and obfuscation attacks are highly effective against current…

The paper introduces THREAT, a novel reasoning-driven framework that efficiently discovers highly effective and targeted jailbreak prompts for LLMs, revealing previously unknown safety vulnerabilities…

The paper introduces an A*-inspired framework to generate highly effective and efficient adversarial prompts that cause LLMs to hallucinate commonsense errors while maintaining the original prompt's i…

DarkLLM introduces a novel framework that uses a Large Language Model (LLM) to translate natural language instructions into flexible, latent adversarial attack vectors, demonstrating a systemic vulner…

The paper proposes a multi-layered security framework to detect and mitigate SQL injection attacks that occur when Large Language Models translate natural language prompts into database queries.

The paper establishes a standardized security assessment framework and develops a multi-layered defensive system, demonstrating that systematic testing and external defenses are crucial for safe LLM d…

The paper introduces SONAR, a prompt sanitization framework that uses natural language inference metrics to identify and remove malicious instructions injected into LLM prompts, achieving near-zero at…

Reflect-Guard enhances LLM safety classifiers by integrating logical self-reflection, significantly improving detection of sophisticated adversarial jailbreak prompts.

The paper demonstrates that using advanced AI agents in an autoresearch loop can discover novel and highly effective adversarial attack algorithms, significantly advancing the state-of-the-art for jai…

This survey provides a comprehensive taxonomy and vulnerability-centric analysis of adversarial attacks targeting Multimodal Large Language Models (MLLMs), offering an explanatory framework for enhanc…

The paper demonstrates that encoding harmful prompts as genuine mathematical problems, rather than just using mathematical formatting, effectively bypasses the safety filters of large language models.

LocalAlign proposes a generalizable prompt injection defense by generating near-target adversarial examples, which enforces a tighter robustness boundary around the correct model response.

The paper introduces an automated framework demonstrating that LLM system instructions are vulnerable to encoding attacks, where structured output requests can bypass safety refusals and leak sensitiv…

The security of LLM agents is critically dependent on their system prompt configuration, which creates a brittle attack surface that can be exploited by attackers inverting the prompt's core assumptio…

The paper introduces PromptFuzz-SC, a novel semantic-character dual-space mutation framework, demonstrating that combining both semantic and character-level attacks significantly improves the robustne…

The paper introduces ARGUS, a defense mechanism that uses provenance-aware decision auditing to protect LLM agents from sophisticated, context-aware prompt injection attacks, significantly reducing th…

The paper introduces ImageProtector, a user-side method that embeds an imperceptible perturbation into images to prevent Multi-modal Large Language Models (MLLMs) from analyzing and extracting sensiti…

The paper introduces Prompt Control-Flow Integrity (PCFI), a priority-aware runtime defense that models LLM prompts as structured segments to intercept prompt injection attacks with high accuracy and…

The paper introduces GuardPhish, a large-scale dataset and evaluation framework, demonstrating that even high-performing open-source LLMs can generate actionable phishing content despite accurate inte…