Language Model Security Database

Post-hoc Large Language Model (LLM) unlearning and guardrailing mechanisms (specifically In-Context Unlearning [ICUL] and standard prompt-based Guardrailing) are vulnerable to information leakage attacks via "Target Masking" and indirect referencing. These systems rely on superficial semantic matching to suppress "forget sets" (specific entities or concepts). Attackers can bypass these restrictions by querying associated properties, relationships, or pseudonyms rather than the explicit target…

Vision Language Models (VLMs) integrated into autonomous driving (AD) systems are vulnerable to a black-box adversarial attack method termed Cascading Adversarial Disruption (CAD). The vulnerability stems from the model's susceptibility to optimized visual perturbations that disrupt the decision-making reasoning chain (perception, prediction, and planning). Attackers can generate adversarial images or physical patches by aligning visual noise with deceptive textual semantics in the model's…

A vulnerability exists in Large Language Model (LLM) routing systems (control planes) that allows for the manipulation of inference flow via adversarial input sequences. LLM routers, which dynamically direct user queries to either "weak" (cheaper) or "strong" (expensive) models based on predicted query complexity, can be bypassed by appending specific, pre-optimized token sequences known as "confounder gadgets." These gadgets artificially inflate the router's complexity score for an input…

The Virus attack method enables attackers to bypass guardrail moderation on fine-tuning data, leading to a significant degradation of safety alignment in large language models (LLMs). This is achieved through a dual-objective data optimization strategy that crafts harmful data undetectable by the guardrail while maximizing their effectiveness in compromising the victim model's safety.

Vision Language Models (VLMs) are vulnerable to visual prompt injection attacks via text-to-image obfuscation. While these models often possess safety guardrails for standard text-based inputs, they fail to apply equivalent safety alignment to textual instructions embedded visually within an image. An attacker can overlay malicious instructions (e.g., requests for illegal acts, hate speech) onto an image file and submit it to the model. The model’s Optical Character Recognition (OCR) or visual…

A vulnerability in LLM-based agents, dubbed AI Agent Injection (AI²), allows attackers to hijack the agent's actions by manipulating the agent's memory retrieval mechanism. The attack involves two main steps: (1) Stealing action-aware knowledge from the agent's memory using crafted adversarial queries targeting the retriever module and (2) Generating Trojan prompts consisting of a Trojan string and hijacking instructions. The Trojan string is designed to manipulate the retriever into…

Jailbreaking vulnerabilities in Large Language Models (LLMs) used in Retrieval-Augmented Generation (RAG) systems allow escalation of attacks from entity extraction to full document extraction and enable the propagation of self-replicating malicious prompts ("worms") within interconnected RAG applications. Exploitation leverages prompt injection to force the LLM to return retrieved documents or execute malicious actions specified within the prompt.

A Cross-Prompt Injection Attack (XPIA) can be amplified by appending a Greedy Coordinate Gradient (GCG) suffix to the malicious injection. This increases the likelihood that a Large Language Model (LLM) will execute the injected instruction, even in the presence of a user's primary instruction, leading to data exfiltration. The success rate of the attack depends on the LLM's complexity; medium-complexity models show increased vulnerability.

LLMs, even when individually assessed as "safe," can be combined by an adversary to achieve malicious outcomes. This vulnerability exploits the complementary strengths of multiple models—a high-capability model that refuses malicious requests and a low-capability model that does not—through task decomposition. Adversaries can either manually decompose tasks into benign (solved by the high-capability model) and easily-malicious subtasks (solved by the low-capability model) or automate the…