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

A research paper by Spiros Tsigkopoulos and Christoforos Ntantogian introduces "LLM-mediated web attacks," a class where attacker-controlled input is transformed by an LLM-integrated application and then reaches traditional web-app sinks, systematized as LLM2X variants (LLM2SQLi, LLM2XSS, LLM2SSTI, LLM2CommandInjection, LLM2IDOR, LLM2CSRF, LLM2XXE, LLM2SSRF). The authors implement TicketOracle, a Flask-based LLM-integrated app, to evaluate LLM2SSRF across five scenarios and seven LLMs, finding substantial model-specific variation in susceptibility. Details →

(A)I Sees What You Don't: Exploiting New Attack Surfaces in Third-Party Mobile Agents

Researchers from Simon Fraser University, CUHK, Shandong University, and QAX's Xingtu Lab (arXiv:2607.00333) demonstrated seven concrete attacks against five open-source mobile AI agent frameworks—AppAgent, AppAgentX, Mobile-Agent-v3, Open-AutoGLM, and MobA—exploiting new attack surfaces in VLM-driven mobile agents. A malicious Android app with no special permissions can inject subliminal, human-invisible on-screen text that the agent's vision model reads, hijacking agent actions and chaining to arbitrary command execution on the host PC driving the agent. Each framework fell to at least six of the seven attacks; the authors report no evidence of the techniques being used outside a controlled setting and no CVEs are assigned. Details →
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