Language Model Security Database

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…

Safety alignment degradation occurs in Large Language Models (LLMs) such as Llama-2, Llama-3, and Qwen-2 when subjected to Supervised Fine-Tuning (SFT) or Continual Pre-Training (CPT) on telecommunications domain datasets (TeleQnA, TeleData, TSpecLLM). The vulnerability arises because benign telecom data—characterized by structured tabular entries, long standardization reports, and complex mathematical formulas—shares gradient update directions with harmful data types. This results in…

Large Language Model (LLM) agents are vulnerable to indirect prompt injection attacks through manipulation of external data sources accessed during task execution. Attackers can embed malicious instructions within this external data, causing the LLM agent to perform unintended actions, such as navigating to arbitrary URLs or revealing sensitive information. The vulnerability stems from insufficient sanitization and validation of external data before it's processed by the LLM.

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…

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…

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…

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), including Llama-3, Falcon-3, Gemma-2, and GPT-4 variants, are susceptible to system prompt extraction attacks. The vulnerability exists due to the models' instruction-following nature, which allows remote attackers to bypass safety guardrails and retrieve the model's hidden system configuration (system prompt) verbatim. This is successfully exploited using an "Extended Sandwich Attack," where an adversarial extraction command is embedded between benign questions…

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…

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…