Denial of service Vulnerabilities

A vulnerability in Large Reasoning Models (LRMs) allows attackers to perform Prompt-Induced Inference-Time Denial-of-Service (PI-DoS) attacks by submitting short, semantically coherent adversarial prompts. These prompts, which often take the form of complex logic puzzles with nested dependencies or contradictory constraints, exploit the adaptive computation mechanism of LRMs to force the model into pathologically long, nearly non-terminating intermediate reasoning traces (e.g., generating…

Audio Large Language Models (ALLMs) integrated into voice agent systems for high-stakes domains (banking, IT support, logistics) are vulnerable to multimodal adversarial attacks via spoken interaction. Adversaries can exploit the model's inherent compliance and contextual awareness through multi-turn dialogue to bypass authentication safeguards, escalate privileges (e.g., unauthorized credit limit increases), exfiltrate sensitive Personally Identifiable Information (PII), and poison…

LLM routing systems are vulnerable to adversarial rerouting attacks where malicious triggers prepended to user queries manipulate the router's model-selection mechanism. Because LLM routers function as classifiers evaluating query complexity to balance computational cost and response quality, an attacker can craft adversarial prefixes that distort the query's latent semantic representation. This exploits the router's decision boundaries, forcing the system to misclassify the input and redirect…

Large Language Models (LLMs) contain a resource consumption vulnerability termed "Overflow," wherein specific non-adversarial, plain-text prompts trigger excessive text generation that saturates the model's output token budget. This vulnerability exploits the model's alignment towards helpfulness and exhaustiveness, alongside tokenizer inefficiencies (e.g., zero-width characters), to force the generation of maximum-length responses (often exceeding 5,000 tokens) from short inputs. This differs…

A stealthy resource exhaustion (Economic Denial-of-Service) vulnerability exists in the multi-turn tool-calling layer of Large Language Model (LLM) agents, particularly those utilizing the Model Context Protocol (MCP). An attacker controlling a third-party tool server can manipulate text-visible fields (such as argument descriptions and error messages) to force the LLM into a prolonged, verbose tool-calling loop. By demanding lengthy, non-semantic outputs (e.g., long comma-separated lists) and…

Large Vision-Language Models (LVLMs) are vulnerable to Physical Prompt Injection Attacks (PPIA), a query-agnostic injection technique delivered via the visual modality. The vulnerability stems from the model's "Vision-Enabled Text Recognition" capabilities and "Identity Sensitivity," where the model interprets text embedded in the physical environment (e.g., printed on signs, posters, or objects) as high-priority instructions rather than passive visual data. An attacker can embed adversarial…

A Denial-of-Service (DoS) vulnerability exists in Large Language Model (LLM) inference services where specially crafted input prompts can trigger excessively long or infinite generation loops ("infinite thinking"). This vulnerability, identified as "ThinkTrap," utilizes derivative-free optimization (CMA-ES) within a continuous surrogate embedding space to circumvent the discrete nature of token inputs. By optimizing a low-dimensional latent vector and projecting it to token sequences, an…

Embodied Artificial Intelligence (AI) agents utilizing Vision-Language Models (VLMs) for perception and planning are vulnerable to Indirect Environmental Jailbreak (IEJ). The vulnerability arises from the system's failure to distinguish between user-issued instructions and text embedded in the physical environment (e.g., writing on walls, sticky notes, or projections). The VLM processes visual text detected in the camera feed as authoritative context or direct commands, allowing a black-box…

Large Language Model (LLM)-based Automated Program Repair (APR) systems—such as SWE-agent, OpenHands, and AutoCodeRover—are vulnerable to adversarial manipulation via crafted bug reports. These systems accept unvetted natural language issue descriptions as trusted input to synthesize code patches. An attacker can exploit this trust by submitting semantically plausible but malicious bug reports designed to mislead the APR agent. By leveraging the semantic gap between natural language…

The GPT-OSS-20B large language model contains critical failures in its alignment and Chain-of-Thought (CoT) reasoning mechanisms, specifically in how it prioritizes numerical objectives and validates procedural structure. The model is vulnerable to "Quant Fever," where explicit numerical targets in a prompt (e.g., "delete 90% of files") override contextual safety constraints (e.g., "do not delete important files"). Furthermore, the model exhibits "Reasoning Procedure Mirage," where harmful…