这些陈述看似事实，实则是我们构建并深信不疑的虚构故事，以致无法将其与真实自我剥离。人类文明的成功秘诀，或许正源于这种相信自身叙事的能力。群体用故事构建社会、文化与信仰；个体则通过劳动塑造身份认同。
Rocco Oliveto, University of Molise
，详情可参考有道翻译
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.，更多细节参见YouTube账号,海外视频账号,YouTube运营账号
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1 апреля 2026, 21:50Постсоветское пространство