# Large Language Model Adversarial Landscape Through the Lens of Attack Objectives

arXiv: https://arxiv.org/abs/2502.02960 · HTML: https://mlsanity.com/paper/2502.02960/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2025.

## Built on by (most cited first, 3 of 3)

- [Human Society-Inspired Approaches to Agentic AI Security: The 4C Framework](https://mlsanity.com/paper/2602.01942/) (2026)
- [LLMs as Deceptive Agents: How Role-Based Prompting Induces Semantic Ambiguity in Puzzle Tasks](https://mlsanity.com/paper/2504.02254/) (2025)
- [Compatibility at a Cost: Systematic Discovery and Exploitation of MCP Clause-Compliance Vulnerabilities](https://mlsanity.com/paper/2603.10163/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) represent a transformative leap in artificial intelligence, enabling the comprehension, generation, and nuanced interaction with human language on an unparalleled scale. However, LLMs are increasingly vulnerable to a range of adversarial attacks that threaten their privacy, reliability, security, and trustworthiness. These attacks can distort outputs, inject biases, leak sensitive information, or disrupt the normal functioning of LLMs, posing significant challenges across various applications. In this paper, we provide a novel comprehensive analysis of the adversarial landscape of LLMs, framed through the lens of attack objectives. By concentrating on the core goals of adversarial actors, we offer a fresh perspective that examines threats from the angles of privacy, integrity, availability, and misuse, moving beyond conventional taxonomies that focus solely on attack techniques. This objective-driven adversarial landscape not only highlights the strategic intent behind different adversarial approaches but also sheds light on the evolving nature of these threats and the effectiveness of current defenses. Our analysis aims to guide researchers and practitioners in better understanding, anticipating, and mitigating these attacks, ultimately contributing to the development of more resilient and robust LLM systems.