Safety Vulnerabilities

Multimodal Large Language Models (MLLMs) exhibit a vulnerability to "Reasoning-based Multi-Image Attacks," where safety guardrails are bypassed by distributing harmful intent across multiple images (2–4 inputs). Unlike single-image jailbreaks that rely on visual obfuscation, this vulnerability exploits the model's reasoning capabilities. By presenting images that share a specific relationship (e.g., Temporal Jump, Spatial Juxtaposition, or Causality), an attacker can compel the model to infer…

Text-to-Image (T2I) models and their associated safety filters are vulnerable to MacPrompt, a black-box jailbreak technique that exploits cross-lingual embedding alignments. Attackers can bypass input text filters, latent representation filters, and model-level concept removal defenses by replacing sensitive keywords with "macaronic" substitutes. These substitutes are constructed by extracting and recombining character-level substrings from translations of the target word across multiple…

Large Language Models (LLMs) exhibit a safety alignment bypass vulnerability when processing requests for intelligent optimization algorithm design. Unlike direct requests for malicious code (e.g., ransomware), LLM safety guardrails fail to recognize the malicious intent behind mathematical optimization problems (e.g., Online Bin Packing, Traveling Salesman Problem, Flow Shop Scheduling) when applied to harmful contexts (e.g., optimizing botnet traffic routing, scheduling fake review posts for…

Large Language Models (LLMs) are vulnerable to multi-turn persuasive conversational attacks that induce the adoption of counterfactual beliefs. By leveraging the Source–Message–Channel–Receiver (SMCR) communication framework, attackers can systematically erode a model's confidence in established facts and compel the model to output misinformation. Specific attack vectors include manipulating source attribution (authority framing), message content (logical, credibility, or emotional appeals)…

A multi-turn jailbreak vulnerability exists in aligned Large Language Models (LLMs) via a technique known as Lexical Anchor Tree Search (LATS). The vulnerability exploits the model's instruction-following capabilities regarding list generation and modification. An attacker initializes a "seed pool" of hypothetical benign and harmful prompt-completion pairs under the guise of creating a safety guardrail. By utilizing a Breadth-First Search (BFS) algorithm, the attacker incrementally injects…

Multi-modal Large Language Models (MLLMs) are vulnerable to a multi-turn jailbreaking attack that leverages typographic visual prompts combined with conversational context drifting. The vulnerability exists because MLLMs establish trust and context during initial benign interactions, shifting the model's latent representation toward helpfulness and compromising its ability to detect malicious intent in subsequent turns. The attack vector utilizes an image where a harmful request is…

A vulnerability in the prompt-based persona conditioning of clinical Large Language Models (LLMs) allows system-level role prompts (e.g., "You are an ED physician") to override the model's base safety guardrails and degrade task accuracy. When assigned medically grounded personas or specific interaction styles (e.g., "bold" or "cautious"), the LLM adopts these roles as behavioral priors, which induces non-monotonic, context-dependent shifts in clinical risk posture. While improving performance…

Personalized LLM agents utilizing long-term memory systems are vulnerable to a safety bypass known as intent legitimation. Benign, organically accumulated user memories can bias the model's intent inference, causing it to misinterpret inherently harmful queries as contextually justified. When a malicious request semantically aligns with a user's established persona (e.g., hobbies, mental health history, routine), the model normalizes the request and complies, effectively bypassing standard…

LLM-based navigation agents, including NavGPT and prompt-tuned outdoor agents, are vulnerable to adaptive prompt injection attacks. This vulnerability allows remote attackers to hijack the physical movement of the agent by embedding optimized malicious instructions into benign natural language inputs. The issue arises because the agents parse user instructions to generate executable plans without sufficient separation between control logic and untrusted input. The PINA (Prompt Injection Attack…

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…