Executive Summary
A peer-reviewed paper surveys threats in LLM-powered systems, including prompt leaking, prompt injection and protocol exploits, and recommends systematic testing and hardening.
What Happened
This peer-reviewed paper surveys security threats in LLM-powered systems, focusing on attack types such as prompt leaking, prompt injection, and broader protocol exploits that can impact data confidentiality and system integrity.[8] Using frameworks like PromptInject, the authors examine how sensitive information can be exposed or manipulated through LLM interactions and recommend systematic testing and hardening approaches for organizations deploying LLMs and AI agents.[8]
Vulnerability / Attack Technique
Prompt injection and protocol exploits (survey)
Technical Mechanism
An academic survey of attack types.
AI Security Category
LLM01:2025 Prompt Injection
Part of the Prompt Injection topic.
Relevant Frameworks
RealGround Analysis
Academic grounding for systematic testing. RealGround analysis: include prompt leaking tests alongside injection tests.
Recommended Controls
- Test for system prompt leakage and injection
Related RealGround Service
Secure AI Agent Build · or start with the free AI Agent Security Scanner.
Sources
Journal of Information Security and Applications (ScienceDirect): https://www.sciencedirect.com/science/article/pii/S2405959525001997
Summary and analysis written by RealGround from the source above; last updated 2026-10-18.