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From Prompt Injection to Web Exploitation: Revisiting Classic Vulnerabilities in LLM-Integrated Applications

arXiv Security Archived Aug 12, 2026 ✓ Full text saved

arXiv:2608.10281v1 Announce Type: new Abstract: 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 calls. This paper introduces LLM-mediated web attacks, a class of attacks in which attacker-controlled input is transformed by an LLM-int

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    Computer Science > Cryptography and Security [Submitted on 10 Aug 2026] From Prompt Injection to Web Exploitation: Revisiting Classic Vulnerabilities in LLM-Integrated Applications Spiros Tsigkopoulos, Christoforos Ntantogian 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 calls. This paper introduces LLM-mediated web attacks, a class of attacks in which attacker-controlled input is transformed by an LLM-integrated application and then reaches traditional web-application sinks. We systematize this attack surface through representative LLM2X variants, including LLM2SQLi, LLM2XSS, LLM2SSTI, LLM2CommandInjection, LLM2IDOR, LLM2CSRF, LLM2XXE, and LLM2SSRF. Our analysis shows that the LLM usually does not create the underlying vulnerability itself; rather, it acts as a mediation layer, and in some tool-enabled settings as a confused deputy, carrying attacker influence into components that trust model-generated or model-influenced content. As an experimental case study, we implement TicketOracle, a Flask-based LLM-integrated web application for evaluating LLM2SSRF across five attack scenarios and seven LLMs. Our results show substantial variation in susceptibility across models, suggesting that exploitation depends both on insecure application architecture and model-specific behavior. We conclude with mitigation strategies across the prompt, model, application, and network layers. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2608.10281 [cs.CR]   (or arXiv:2608.10281v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2608.10281 Focus to learn more Submission history From: Christoforos Ntantogian [view email] [v1] Mon, 10 Aug 2026 22:24:16 UTC (339 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Aug 12, 2026
    Archived
    Aug 12, 2026
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