Language Model Security Database

Improper restriction of the "Capability Space" in Large Language Model (LLM) applications allows remote attackers to manipulate application behavior through "Goal Deviation" attacks. This vulnerability arises when developers rely on the broad capabilities of a foundational model (e.g., GPT-4, LLaMA) without implementing sufficient negative constraints or disabling default plugins (e.g., DALL-E, Web Search) in the system prompt. Attackers can exploit this via natural language inputs to trigger…

Large Language Models (LLMs) from multiple vendors exhibit vulnerabilities to jailbreaking techniques that bypass safety guardrails, enabling the automated generation of highly persuasive phishing content specifically targeted at elderly victims. By employing "Roleplay Authority" (posing as researchers) or "Safety Turned Off" (explicit meta-instructions) prompting strategies, attackers can coerce the models into producing social engineering emails—such as fake government benefit notifications…

Large Language Models (LLMs), specifically GPT-3.5-turbo, LLaMA3-8B-instruct, and DeepSeek-R1-Distill-Qwen-7B, are vulnerable to a "Self-Harm" jailbreak attack (Self-HarmLLM). This vulnerability exploits the model's ability to understand its own safety boundaries to generate adversarial inputs against itself. An attacker utilizes a two-session approach: in the first session (Mitigation Session), the attacker instructs the model to rewrite a harmful query into a "Mitigated Harmful Query"…

A vulnerability exists in certain Large Language Models and diffusion models due to discontinuities in their latent space, which arise from data sparsity during training. An attacker can craft inputs containing lexically rare or semantically ambiguous constructs to guide the model's inference process toward these unstable, poorly-conditioned regions. This technique, termed "Alignment Degradation Induction," can degrade or bypass safety alignment mechanisms. Through iterative, multi-turn…

Large Language Models (LLMs) are vulnerable to Linguistic Style Jailbreaks, a technique where an attacker reframes a harmful prompt using specific linguistic tones—such as politeness, fear, curiosity, or compassion—to bypass safety guardrails. While standard safety alignment (RLHF) effectively filters harmful requests phrased in neutral or hostile tones, it fails to generalize to prompts where the semantic intent remains harmful but the stylistic framing triggers compliant, helpful, or…

A vulnerability in Large Language Models (LLMs) allows for systematic jailbreaking through a meta-optimization framework called AMIS (Align to MISalign). The attack uses a bi-level optimization process to co-evolve both the jailbreak prompts and the scoring templates used to evaluate them.

The JPRO (Automated Multimodal Jailbreaking via Multi-Agent Collaboration) framework exploits a vulnerability in Large Vision-Language Models (VLMs) related to insufficient cross-modal safety alignment and lack of maliciousness sustainability in multi-turn dialogues. The attack leverages a multi-agent system (Planner, Attacker, Modifier, Verifier) to automate the generation of adversarial image-text pairs. By employing hybrid tactics—such as combining role-playing with malicious content…

A safety bypass vulnerability, dubbed "Ninja" (Needle-in-a-haystack jailbreak), exists in long-context Large Language Models (LLMs). The vulnerability exploits a degradation in safety alignment that occurs when a harmful goal is embedded within a massive, benign context window. Unlike traditional adversarial attacks that use unintelligible strings or "many-shot" attacks that use harmful examples, this method utilizes thematically relevant but innocuous text (the "haystack"). The attack…

Multiple open-weight Large Language Models (LLMs)—specifically those prioritizing capability over safety alignment—exhibit a critical vulnerability to adaptive multi-turn prompt injection and jailbreak attacks. While these models effectively reject isolated, single-turn adversarial inputs (averaging ~13.11% Attack Success Rate), they fail to maintain safety guardrails and policy enforcement across extended conversational contexts. By leveraging iterative strategies such as "Crescendo" (gradual…

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.