Data Security Vulnerabilities

A vulnerability in fine-tuning-based large language model (LLM) unlearning allows malicious actors to craft manipulated forgetting requests. By subtly increasing the frequency of common benign tokens within the forgetting data, the attacker can cause the unlearned model to exhibit unintended unlearning behaviors when these benign tokens appear in normal user prompts, leading to a degradation of model utility for legitimate users. This occurs because existing unlearning methods fail to effectively distinguish between benign tokens and those truly related to the target knowledge being unlearned.

DNA language models, such as the Evo series, are vulnerable to jailbreak attacks that coerce the generation of DNA sequences with high homology to known human pathogens. The GeneBreaker framework demonstrates this by using a combination of carefully crafted prompts leveraging high-homology non-pathogenic sequences and a beam search guided by pathogenicity prediction models (e.g., PathoLM) and log-probability heuristics. This allows bypassing safety mechanisms and generating sequences exceeding 90% similarity to target pathogens.

Large Language Models (LLMs) are vulnerable to a novel privacy jailbreak attack, dubbed PIG (Privacy Jailbreak Attack on LLMs via Gradient-based Iterative In-Context Optimization). PIG leverages in-context learning and gradient-based iterative optimization to extract Personally Identifiable Information (PII) from LLMs, bypassing built-in safety mechanisms. The attack iteratively refines a crafted prompt based on gradient information, focusing on tokens related to PII entities, thereby increasing the likelihood of successful PII extraction.

Large Language Models (LLMs) employing alignment-based defenses against prompt injection and jailbreak attacks exhibit vulnerability to an informed white-box attack. This attack, termed Checkpoint-GCG, leverages intermediate model checkpoints from the alignment training process to initialize the Greedy Coordinate Gradient (GCG) attack. By using each checkpoint as a stepping stone, Checkpoint-GCG successfully finds adversarial suffixes that bypass defenses achieving significantly higher attack success rates than standard GCG initialized with naive methods. This is particularly impactful as Checkpoint-GCG can discover universal adversarial suffixes effective across multiple inputs.

Large Language Model (LLM)-based Multi-Agent Systems (MAS) are vulnerable to intellectual property (IP) leakage attacks. An attacker with black-box access (only interacting via the public API) can craft adversarial queries that propagate through the MAS, extracting sensitive information such as system prompts, task instructions, tool specifications, number of agents, and system topology.

Large Language Models (LLMs) employing safety mechanisms based on supervised fine-tuning and preference alignment exhibit a vulnerability to "steering" attacks. Maliciously crafted prompts or input manipulations can exploit representation vectors within the model to either bypass censorship ("refusal-compliance vector") or suppress the model's reasoning process ("thought suppression vector"), resulting in the generation of unintended or harmful outputs. This vulnerability is demonstrated across several instruction-tuned and reasoning LLMs from various providers.

A vulnerability exists in Large Language Model (LLM) agents that allows attackers to manipulate the agent's reasoning process through the insertion of strategically placed adversarial strings. This allows attackers to induce the agent to perform unintended malicious actions or invoke specific malicious tools, even when the initial prompt or instruction is benign. The attack exploits the agent's reliance on chain-of-thought reasoning and dynamically optimizes the adversarial string to maximize the likelihood of the agent incorporating malicious actions into its reasoning path.

A vulnerability exists in large language models (LLMs) where insufficient sanitization of system prompts allows attackers to extract sensitive information embedded within those prompts. Attackers can use an agentic approach, employing multiple interacting LLMs (as demonstrated in the referenced research), to iteratively refine prompts and elicit confidential data from the target LLM's responses. The vulnerability is exacerbated by the LLM's ability to infer context from seemingly innocuous prompts.

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.

CVE-2024-XXXX