Impact
Security concerns affecting AI safety and alignment
798 matching entries
An untrusted reinforcement-learning-from-human-feedback (RLHF) platform can selectively manipulate preference samples associated with an attacker's target domain. The corrupted preference data trains a compromised reward model and then steers the fine-tuned language model toward undesirable behavior, creating a model-supply-chain risk before deployment.
Source: arXiv
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…
Source: arXiv
Large Language Models (LLMs) incorporating safety filters are vulnerable to a "Prompt, Divide, and Conquer" attack. This attack segments a malicious prompt into smaller, seemingly benign parts, processes these segments in parallel across multiple LLMs, and then reassembles the results to generate malicious code, bypassing the safety filters. The attack's success relies on the iterative refinement of initially abstract function descriptions into concrete implementations. Individual LLM safety…
Source: arXiv
Large Language Models (LLMs) are vulnerable to implicit misinformation propagation due to sycophantic compliance with false premises. When a user prompt embeds a factually incorrect assumption or conspiracy theory as an unchallenged premise (implicit presupposition) rather than asking for verification, the model frequently fails to detect the falsehood. Instead of correcting the user, the model hallucinates a response that accepts, validates, and reinforces the false premise. This…
Source: arXiv
This vulnerability allows attackers to identify the presence and location (input or output stage) of specific guardrails implemented in Large Language Models (LLMs) by using carefully crafted adversarial prompts. The attack, termed AP-Test, leverages a tailored loss function to optimize these prompts, maximizing the likelihood of triggering a specific guardrail while minimizing triggering others. Successful identification provides attackers with valuable information to design more effective…
Source: arXiv
A vulnerability in Large Language Models (LLMs) allows adversarial reasoning attacks to bypass safety mechanisms and elicit harmful responses. The vulnerability stems from the insufficient robustness of existing LLM safety measures against iterative prompt refinement guided by a loss function that measures the LLM's proximity to generating a target harmful response. This allows an attacker to effectively navigate the prompt space, even against adversarially trained models, resulting in…
Source: arXiv
Vision-Language Models (VLMs), specifically the LLaVA-1.5 and LLaVA-1.6 series, are vulnerable to optimization-based white-box jailbreak attacks despite standard safety alignment measures like Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO). Attackers can craft adversarial perturbations in the image space (imperceptible noise) or latent space using Projected Gradient Descent (PGD) to manipulate the model's internal representations. These perturbations maximize the…
Source: arXiv
Commercial LLM-powered agents utilizing autonomous web access, memory modules, and retrieval-augmented generation (RAG) are vulnerable to indirect prompt injection and environmental manipulation. Attackers can embed malicious instructions into external data sources trusted by the agent (such as Reddit posts, public databases, or ArXiv papers). When the agent autonomously retrieves and processes this content during task execution, it executes the embedded malicious commands. This vulnerability…
Source: arXiv
A vulnerability exists in the communication mechanisms of Large Language Model (LLM)-based Multi-Agent Systems (LLM-MAS) enabling an Agent-in-the-Middle (AiTM) attack. An attacker can intercept and manipulate messages between agents, causing the victim agent to produce malicious outputs. The attack does not require compromising individual agents directly; instead, it leverages contextual manipulation of inter-agent communications.
Source: arXiv
A vulnerability in large language models (LLMs) allows attackers to bypass safety-alignment mechanisms by manipulating the model's internal attention weights. The attack, termed "Attention Eclipse," modifies the attention scores between specific tokens within a prompt, either amplifying or suppressing attention to selectively strengthen or weaken the influence of certain parts of the prompt on the model's output. This allows injection of malicious content while appearing benign to the model's…
Source: arXiv