Language Model Security Database

Retrieval-Augmented Generation (RAG) systems are vulnerable to iterative knowledge-extraction attacks designed to reconstruct the underlying private knowledge base. The vulnerability exists due to the decoupled optimization of the retrieval and generation phases. Attackers can craft adversarial queries consisting of two distinct components: an "Information" component (optimized via gradient descent or random sampling to steer embeddings toward specific, diverse regions of the vector space) and…

Text scoring models, including dense retrievers, rerankers, and reward models, are vulnerable to score manipulation attacks via search-based discrete perturbations and content injection. An attacker can systematically modify candidate texts using rudimentary string manipulations, gradient-guided token swaps (e.g., HotFlip), masked language modeling (MLM) swaps, or query/sentence injections to spuriously increase model scores. This structural failure condition allows an irrelevant passage or a…

Large Language Models (LLMs), specifically Mistral 7B, Gemma 2 9B, and Llama 3 8B, are vulnerable to safety filter bypass via "Emoji-Based Jailbreaking." This adversarial prompt engineering technique exploits the model's tokenization and internal representation of Unicode emoji characters. By utilizing "emoji stuffing" (inserting emojis between textual tokens) or "emoji chaining" (using sequences of emojis as semantic proxies for sensitive terms), attackers can evade keyword-based safety…

Large Language Models (LLMs) finetuned from open-weight pretrained sources inherit adversarial vulnerabilities encoded in the pretrained model's internal representations. An attacker with white-box access to a pretrained model (e.g., Llama-2, Llama-3) can identify linearly separable features in the hidden states that correlate with "transferable" jailbreak prompts. By exploiting these features using a Probe-Guided Projection (PGP) attack, the attacker can optimize adversarial suffixes on the…

A jailbreak vulnerability, termed Embedded Jailbreak Template (EJT), allows for the generation of harmful content by bypassing the safety mechanisms of Large Language Models (LLMs). The attack uses a generator LLM to contextually integrate a harmful query into a pre-existing jailbreak template. Unlike fixed templates which insert a query into a static placeholder, EJT rewrites multiple parts of the template to embed the harmful intent naturally. This process preserves the original template's…

The KG-DF (Knowledge Graph Defense Framework) contains a logic vulnerability in its Semantic Parsing Module, specifically within the keyword extraction phase defined as $K_{core} = \text{LLM}(P_{prompt})$. The framework relies on a Large Language Model (e.g., GPT-3.5-turbo) to distill user input into keywords ($K_{core}$), which are then embedded to retrieve security warning triples ($T_{match}$) from a Knowledge Graph.

Large Language Models (LLMs) that use special tokens to define conversational structure (e.g., via chat templates) are vulnerable to a jailbreak attack named MetaBreak. An attacker can inject these special tokens, or regular tokens with high semantic similarity in the embedding space, into a user prompt. This manipulation allows the attacker to bypass the model's internal safety alignment and external content moderation systems. The attack leverages four primitives: 1. Response Injection…

Large Language Models (LLMs), including Llama 2, Mistral, and Vicuna, are susceptible to a white-box adversarial attack that circumvents safety alignment mechanisms (such as RLHF). The vulnerability exists due to the models' susceptibility to intrinsic optimization of adversarial suffixes using Exponentiated Gradient Descent (EGD). Unlike previous methods that rely on inefficient discrete token searches (e.g., Greedy Coordinate Gradient) or standard projected gradient descent, this attack…

Sparse Autoencoders (SAEs), utilized for interpreting the internal residual stream activations of Large Language Models (LLMs) into human-understandable concepts, are vulnerable to adversarial input perturbations. By employing gradient-based optimization techniques adapted for SAEs (specifically a generalized Greedy Coordinate Gradient), an attacker can craft inputs via suffix appending or token replacement that manipulate the SAE's latent feature activations. This vulnerability allows for the…

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…