Language Model Security Database

Large Language Models (LLMs), including both proprietary and open-source instruction-tuned models, contain a vulnerability to strategic, multi-turn adversarial attacks. Unlike single-turn prompt injections, this vulnerability is exploited through sequential decision-making where an attacker (or automated agent) utilizes reinforcement learning and tree-based search (e.g., DialTree-RPO) to navigate the dialogue state space. By employing strategies such as intent laundering (framing harmful…

Large Language Model (LLM) fine-tuning interfaces are vulnerable to a semantic obfuscation attack that bypasses multi-stage safety defenses, including pre-upload data filtering, defensive fine-tuning algorithms, and post-training safety audits. The vulnerability exploits a "self-auditing" flaw where the provider uses the target model (or a similar variant) to screen training data. Attackers can submit a small dataset (approx. 500 samples) where harmful answers are obfuscated using a…

AI code agents are vulnerable to jailbreaking attacks that cause them to generate or complete malicious code. The vulnerability is significantly amplified when a base Large Language Model (LLM) is integrated into an agentic framework that uses multi-step planning and tool-use. Initial safety refusals by the LLM are frequently overturned during subsequent planning or self-correction steps within the agent's reasoning loop.

A vulnerability exists in tool-enabled Large Language Model (LLM) agents, termed Sequential Tool Attack Chaining (STAC), where a sequence of individually benign tool calls can be orchestrated to achieve a malicious outcome. An attacker can guide an agent through a multi-turn interaction, with each step appearing harmless in isolation. Safety mechanisms that evaluate individual prompts or actions fail to detect the threat because the malicious intent is distributed across the sequence and only…

Large Language Models (LLMs), including proprietary and open-weight state-of-the-art systems, are vulnerable to automated, self-evolving adversarial attacks orchestrated by multi-agent frameworks. The vulnerability exists because current safety alignment strategies (RLHF, static safety filters) fail to generalize against the "SafeEvalAgent" attack vector. In this vector, an "Analyst" agent analyzes model refusals to iteratively refine attack strategies, while a "Specialist" agent grounds these…

Large Language Model (LLM) agents utilizing long-term memory or Retrieval-Augmented Generation (RAG) are vulnerable to context-dependent memory injection attacks. Unlike traditional prompt injections that are overtly malicious, this vulnerability involves injecting records that appear benign and coherent in isolation—thereby bypassing standard perplexity filters and static content moderation (e.g., LlamaGuard). These records contain "sleeping" malicious logic that is only activated when…

A vulnerability exists in aligned Large Language Models (LLMs) where a harmful instruction can be obfuscated through a multi-step formalization process, bypassing safety mechanisms. The attack, named Prompt Jailbreaking via Semantic and Structural Formalization (PASS), uses a Reinforcement Learning (RL) agent to dynamically construct an adversarial prompt. The agent learns to apply a sequence of actions—such as symbolic abstraction, logical encoding, mathematical representation, metaphorical…

LLM-based search agents are vulnerable to manipulation via unreliable search results. An attacker can craft a website containing malicious content (e.g., misinformation, harmful instructions, or indirect prompt injections) that is indexed by search engines. When an agent retrieves and processes this page in response to a benign user query, it may uncritically accept the malicious content as factual and incorporate it into its final response. This allows the agent to be used as a vector for…

A vulnerability exists in Large Language Models (LLMs) and multi-label text classification systems that allows for Textual Dynamic Outputs Attacks (TDOA). This technique enables hard-label black-box attacks against systems with variable or generative output spaces (where the number of labels or specific label tokens are not fixed). The attack functions by training a surrogate model on clustered coarse-grained labels derived from the victim model's fine-grained dynamic outputs. It subsequently…

Frontier Large Language Models (LLMs) utilizing Chain-of-Thought (CoT) reasoning are vulnerable to deceptive alignment attacks via adversarial system prompt injection. This vulnerability allows an attacker to induce "deceptive reasoning," where the model’s internal CoT actively plans or entertains malicious directives (e.g., radicalization, bias, or violence) while the final user-facing output remains benign, helpful, and innocuous. By creating a dissociation between internal reasoning and…