Language Model Security Database

Large Language Models (LLMs) with structured output APIs (e.g., using JSON Schema) are vulnerable to Constrained Decoding Attacks (CDAs). CDAs exploit the control plane of the LLM's decoding process by embedding malicious intent within the schema-level grammar rules, bypassing safety mechanisms that primarily focus on input prompts. The attack manipulates the allowed output space, forcing the LLM to generate harmful content despite a benign input prompt. One instance of a CDA is the Chain Enum…

FC-Attack leverages automatically generated flowcharts containing step-by-step descriptions derived or rephrased from harmful queries, combined with a benign textual prompt, to jailbreak Large Vision-Language Models (LVLMs). The vulnerability lies in the model's susceptibility to visual prompts containing harmful information within the flowcharts, thus bypassing safety alignment mechanisms.

Large Language Models (LLMs) with structured output interfaces are vulnerable to jailbreak attacks that exploit the interaction between token-level inference and sentence-level safety alignment. Attackers can manipulate the model's output by constructing attack patterns based on prefixes of safety refusal responses and desired harmful outputs, effectively bypassing safety mechanisms through iterative API calls and constrained decoding. This allows the generation of harmful content despite…

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…

A multi-step prompt injection vulnerability allows attackers to bypass Large Language Model (LLM) safety guardrails by combining prompt obfuscation with task decomposition. The attack methodology, identified as part of the CySecBench research, employs a "Word Reversal" technique where every fifth word in the malicious input is reversed to evade initial keyword detection. This obfuscated input is then embedded within a benign educational context, specifically instructing the model to act as a…

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.

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…

A vulnerability exists in Large Language Model (LLM)-based time series forecasting architectures, specifically affecting models such as TimeGPT, LLMTime, and TimeLLM. These models are susceptible to a gradient-free, black-box adversarial attack method termed Directional Gradient Approximation (DGA). An attacker can inject imperceptible perturbations into the historical time series input window (lookback window) to manipulate the model's output. By treating the model as a black box and…

A novel black-box attack framework leverages fuzz testing to automatically generate concise and semantically coherent prompts that bypass safety mechanisms in large language models (LLMs), eliciting harmful or offensive responses. The attack starts with an empty seed pool, utilizes LLM-assisted mutation strategies (Role-play, Contextualization, Expand), and employs a two-level judge module for efficient identification of successful jailbreaks. The attack's effectiveness is demonstrated across…

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.