Language Model Security Database

Mamba-2 and hybrid Transformer-Mamba-2 distilled Large Language Model (LLM) architectures exhibit a distinct architectural susceptibility to Latent Injection and ANSI Escape sequence prompt injection attacks. Comparative analysis reveals that models incorporating Mamba state-space components (specifically distilled variants like Llamba-3B and base Mamba models) fail to maintain adversarial robustness levels comparable to pure Transformer baselines (such as Llama-3.2) when subjected to indirect…

Large Language Models (LLMs) integrated with external retrieval mechanisms (e.g., Retrieval-Augmented Generation (RAG), web search, or email processing) are vulnerable to Indirect Prompt Injection. This vulnerability occurs when an LLM consumes input from untrusted external sources—such as websites, code repositories, or incoming emails—that contain embedded adversarial prompts. Unlike direct injection, where the user attacks the model, here the "poisoned" data is retrieved by the system…

Large Language Models (LLMs) utilized for static code analysis, code review, and autonomous software engineering exhibit a cognitive vulnerability termed "Abstraction Bias." When processing code that structurally resembles common algorithmic patterns (e.g., standard sorting algorithms, helper functions, or mathematical formulas), the model relies on high-level memorized representations of the algorithm's intent rather than analyzing the specific local logic. Adversaries can exploit this by…

A vulnerability exists in Large Language Model (LLM)-based Multi-Agent Systems (MAS) that allows a malicious agent to covertly disrupt collaborative decision-making processes without triggering standard safety filters or anomaly detection. This "intention-hiding" attack occurs when an agent adopts a persona that appears linguistically fluent and role-consistent but strategically steers the group toward incorrect outcomes or resource exhaustion. The attacker leverages specific semantic…

Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) are vulnerable to "Secondary Risks," a class of non-adversarial failures where the model generates harmful, misleading, or unsafe outputs in response to benign, non-malicious user prompts. Unlike jailbreaks which require adversarial inputs, secondary risks arise from imperfect generalization and alignment failures during standard interactions. This vulnerability manifests primarily in two primitives: 1. Excessive…

Large Language Model (LLM) agents capable of invoking external APIs are vulnerable to intent integrity violations. When an agent receives natural language instructions that are ambiguous, underspecified, or contain values not supported by the underlying API schema, the agent frequently fails to preserve user intent. Instead of rejecting the request or asking for clarification, the model may hallucinate parameter values, map unsupported requests to unsafe defaults, or execute actions on…

A vulnerability exists in the safety alignment mechanisms of Large Language Models (LLMs) (including GPT-4, Claude 3, Gemini, and Qwen families) leading to "Implicit Harm." Unlike traditional jailbreaks that use overtly harmful queries, this vulnerability allows remote attackers to coerce the model into providing factually incorrect, plausible, and dangerous responses to benign-looking inputs. By employing "JailFlip" techniques—specifically constructed affirmative-type or denial-type queries…

Large Language Models (LLMs) that utilize byte-stream parsing or structural extraction to process PDF files—specifically the OpenAI GPT and Anthropic Claude families—are vulnerable to adversarial text injection via imperceptible "phantom tokens." This vulnerability exploits the disconnect between how PDF viewers render documents for humans (visual layer) and how LLMs extract text from the PDF operator stream (data layer). Attackers can manipulate standard PDF text-showing operators (TJ and Tj)…

Large Language Models (LLMs) utilizing Chain-of-Thought (CoT) prompting are vulnerable to input perturbations that decouple intermediate reasoning from the final answer. An attacker can generate adversarial examples using gradient-based optimization (targeting specific loss functions that maximize reasoning divergence while minimizing answer loss) to induce "Right Answer, Wrong Reasoning" behaviors. This vulnerability manifests through two primary attack vectors: 1. Token-level perturbations…

A vulnerability exists in Vision-Language Models (VLLMs) that allows for transferable, targeted adversarial attacks. Attackers can generate adversarial image perturbations using an ensemble of open-source surrogate models (primarily CLIP-based visual encoders) which effectively transfer to proprietary, black-box VLLMs. The attack leverages a specific optimization framework that combines a Visual Contrastive Loss with multiple positive/negative visual examples, rather than relying solely on…