VQV Signal
Large Language Models (LLMs) integrated into web applications can be exploited through attacks where user input influences backend actions like database queries and API calls. This paper introduces LLM-mediated web attacks, highlighting new security risks in AI-powered systems.
Large Language Models (LLMs) integrated into web applications can be exploited through attacks where user input influences backend actions like database queries and API calls. This paper introduces LLM-mediated web attacks, highlighting new security risks in AI-powered systems.
AI-assisted summary based on the listed source.
Large Language Models are increasingly integrated into web applications through chatbots, tool-calling pipelines, and agentic workflows. In these systems, user input may influence not only generated text, but also backend actions such as database queries, HTTP requests, file operations, template rendering, or API...
As LLMs become common in web apps, understanding these novel attack vectors is crucial to securing AI-driven workflows and preventing exploitation of backend systems. This research revisits classic vulnerabilities in the context of AI integration, emphasizing the need for updated security measures.
Security-conscious readers may want to review the source and watch for practical exposure or mitigation details.
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VQV surfaced this signal because it is recent, relevant to AI Security, connected to arXiv.
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