Application Layer Vulnerabilities

Large Language Model (LLM) systems integrated with private enterprise data, such as those using Retrieval-Augmented Generation (RAG), are vulnerable to multi-stage prompt inference attacks. An attacker can use a sequence of individually benign-looking queries to incrementally extract confidential information from the LLM's context. Each query appears innocuous in isolation, bypassing safety filters designed to block single malicious prompts. By chaining these queries, the attacker can reconstruct sensitive data from internal documents, emails, or other private sources accessible to the LLM. The attack exploits the conversational context and the model's inability to recognize the cumulative intent of a prolonged, strategic dialogue.

Large Language Models (LLMs) employing safety filters designed to prevent generation of content related to self-harm and suicide can be bypassed through multi-step adversarial prompting. By reframing the request as an academic exercise or hypothetical scenario, users can elicit detailed instructions and information that could facilitate self-harm or suicide, despite initially expressing harmful intent. This vulnerability lies in the inadequacy of existing safety filters to consistently recognize and prevent harmful outputs despite shifts in conversational context.

A vulnerability exists in Large Language Models, including GPT-3.5 and GPT-4, where safety guardrails can be bypassed using Trojanized prompt chains within a simulated educational context. An attacker can establish a benign, pedagogical persona (e.g., a curious student) over a multi-turn dialogue. This initial context is then exploited to escalate the conversation toward requests for harmful or restricted information, which the model provides because the session's context is perceived as safe. The vulnerability stems from the moderation system's failure to detect semantic escalation and topic drift within an established conversational context. Two primary methods were identified: Simulated Child Confusion (SCC), which uses a naive persona to ask for dangerous information under a moral frame (e.g., "what not to do"), and Prompt Chain Escalation via Literary Devices (PCELD), which frames harmful concepts as an academic exercise in satire or metaphor.

Multimodal Large Language Models (MLLMs) are vulnerable to visual contextual attacks, where carefully crafted images and accompanying text prompts can bypass safety mechanisms and elicit harmful responses. The vulnerability stems from the MLLM's ability to integrate visual and textual context to generate outputs, allowing attackers to create realistic scenarios that subvert safety filters. Specifically, the attack leverages image-driven context injection to construct deceptive multi-turn conversations that gradually lead the MLLM to produce unsafe responses.

Large language models (LLMs) protected by multi-stage safeguard pipelines (input and output classifiers) are vulnerable to staged adversarial attacks (STACK). STACK exploits weaknesses in individual components sequentially, combining jailbreaks for each classifier with a jailbreak for the underlying LLM to bypass the entire pipeline. Successful attacks achieve high attack success rates (ASR), even on datasets of particularly harmful queries.

Large Language Models (LLMs) are vulnerable to adaptive jailbreaking attacks that exploit their semantic comprehension capabilities. The MEF framework demonstrates that by tailoring attacks to the model's understanding level (Type I or Type II), evasion of input, inference, and output-level defenses is significantly improved. This is achieved through layered semantic mutations and dual-ended encryption techniques, allowing bypass of security measures even in advanced models like GPT-4o.

Large Language Model (LLM) agents are vulnerable to indirect prompt injection attacks through manipulation of external data sources accessed during task execution. Attackers can embed malicious instructions within this external data, causing the LLM agent to perform unintended actions, such as navigating to arbitrary URLs or revealing sensitive information. The vulnerability stems from insufficient sanitization and validation of external data before it's processed by the LLM.

End-to-end Large Audio-Language Models (LALMs) are vulnerable to AudioJailbreak, a novel attack that appends adversarial audio perturbations ("jailbreak audios") to user prompts. These perturbations, even when applied asynchronously and without alignment to the user's speech, can manipulate the LALM's response to generate adversary-desired outputs that bypass safety mechanisms. The attack achieves universality by employing a single perturbation effective across different prompts and robustness to over-the-air transmission by incorporating reverberation effects during perturbation generation. Even with stealth strategies employed to mask malicious intent, the attack remains highly effective.

Large Language Models (LLMs) used for evaluating text quality (LLM-as-a-Judge architectures) are vulnerable to prompt-injection attacks. Maliciously crafted suffixes appended to input text can manipulate the LLM's judgment, causing it to incorrectly favor a predetermined response even if another response is objectively superior. Two attack vectors are identified: Comparative Undermining Attack (CUA), directly targeting the final decision, and Justification Manipulation Attack (JMA), altering the model's generated reasoning.

Large Language Model (LLM)-based Multi-Agent Systems (MAS) are vulnerable to intellectual property (IP) leakage attacks. An attacker with black-box access (only interacting via the public API) can craft adversarial queries that propagate through the MAS, extracting sensitive information such as system prompts, task instructions, tool specifications, number of agents, and system topology.