Security and Privacy Challenges of Large Language Models: A Survey

Analysis

Why This Paper Matters

Large language models (LLMs) have rapidly become ubiquitous in natural language processing, powering applications from text generation to question answering. However, their widespread adoption has exposed critical security and privacy vulnerabilities that can have severe real-world consequences. This survey, published in ACM Computing Surveys, arrives at a crucial time when the AI community is grappling with how to deploy LLMs safely and responsibly. With 576 citations, it has already become a key reference for understanding the threat landscape.

The paper's significance lies in its comprehensive and structured examination of security and privacy challenges specific to LLMs. Unlike broader AI security surveys, it focuses on the unique attack surfaces of LLMs, such as jailbreaking, data poisoning, and personally identifiable information (PII) leakage. By also exploring application-based risks in high-stakes domains like transportation, education, and healthcare, the authors highlight the practical implications of these vulnerabilities. This domain-specific analysis is particularly valuable for practitioners who need to assess risks in their own deployments.

Moreover, the survey bridges the gap between attack research and defense mechanisms, offering a holistic view of the current state of LLM security. It not only catalogs known attacks but also reviews potential defenses, thereby equipping researchers and developers with the knowledge to build more robust systems. The identification of research gaps and future directions further accelerates progress by guiding the community's efforts toward unresolved challenges.

Technical Contributions

• Comprehensive taxonomy of LLM security and privacy attacks: The survey categorizes attacks into jailbreaking, data poisoning, PII leakage, and others, providing a clear framework for understanding the threat landscape.
• Application-based risk assessment: It analyzes risks in specific domains (transportation, education, healthcare), illustrating how LLM vulnerabilities translate into domain-specific harms.
• Review of defense mechanisms: The paper surveys existing defenses, from input sanitization to differential privacy, and discusses their effectiveness and limitations.
• Identification of research gaps: It outlines open problems and future research directions, such as the need for standardized benchmarks and robust evaluation metrics.

Results

As a survey, the paper does not present new experimental results. Instead, it synthesizes findings from a wide range of studies, providing a qualitative assessment of the state of the art. It reports on the prevalence and severity of various attack types as documented in the literature, and evaluates defenses based on reported effectiveness. The survey also highlights the lack of comprehensive benchmarks and the need for more rigorous evaluation methodologies. While no concrete metrics are introduced, the paper's organization and analysis serve as a valuable meta-result, revealing gaps in current research.

Significance

The survey's significance extends beyond academia. For practitioners, it offers a practical guide to understanding and mitigating LLM risks, which is essential for responsible deployment. For researchers, it provides a roadmap for future work, emphasizing areas such as robust defenses, privacy-preserving techniques, and cross-domain risk analysis. By raising awareness of these challenges, the paper contributes to the development of trustworthy AI systems and informs policy discussions on AI safety and privacy. Given the rapid evolution of LLMs, this survey will likely remain a foundational reference for years to come.