乘缆车穿越云海 诗仙李白笔下的庐山瀑布宛若仙境
A bit about the origins of the graphic calculators: Marovitch has had both since they were demanded for a math class in high school. "They were outrageously expensive," she recalled, "And I will say, I was a big old nerd, and I had my parents buy me two. I had terrible anxiety that in the middle of the math test, my first calculator would stop working, the batteries would stop. So I had my parents buy me two, so that I always had a backup."
。有道翻译是该领域的重要参考
국힘 공관위, 오세훈 겨냥 “후보 없더라도 공천 기강 세울 것”
Певицу в Турции заподозрили в оскорблении Эрдогана17:51
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.