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However, the failure modes we document differ importantly from those targeted by most technical adversarial ML work. Our case studies involve no gradient access, no poisoned training data, and no technically sophisticated attack infrastructure. Instead, the dominant attack surface across our findings is social: adversaries exploit agent compliance, contextual framing, urgency cues, and identity ambiguity through ordinary language interaction. [135] identify prompt injection as a fundamental vulnerability in this vein, showing that simple natural language instructions can override intended model behavior. [127] extend this to indirect injection, demonstrating that LLM integrated applications can be compromised through malicious content in the external context, a vulnerability our deployment instantiates directly in Case Studies #8 and #10. At the practitioner level, the Open Worldwide Application Security Project’s (OWASP) Top 10 for LLM Applications (2025) [90] catalogues the most commonly exploited vulnerabilities in deployed systems. Strikingly, five of the ten categories map directly onto failures we observe: prompt injection (LLM01) in Case Studies #8 and #10, sensitive information disclosure (LLM02) in Case Studies #2 and #3, excessive agency (LLM06) across Case Studies #1, #4 and #5, system prompt leakage (LLM07) in Case Study #8, and unbounded consumption (LLM10) in Case Studies #4 and #5. Collectively, these findings suggest that in deployed agentic systems, low-cost social attack surfaces may pose a more immediate practical threat than the technical jailbreaks that dominate the adversarial ML literature.
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这场闹剧背后，人们不禁要问：在头部直播间宣称严格筛选、亲自试用的承诺下，下一个“优思益”何时会再次出现？
Главный тренер украинской футбольной команды обвинен в нарушении этикета 20:42