Verified65% confidenceFactExact time
Multiple academic studies in 2024 indicate that current mainstream LLMs still have limited resistance to carefully crafted prompt injection attacks.
3
Sources
65%
Confidence
Long-term
Relevance
7/12/2026
First Seen
Sources
Related Claims
Unverified2024年的研究表明,即使经过专门的安全对齐训练,主流模型对精心构造的注入攻击的防御成功率仍不足90%71% similarUnverified2023年多项研究证明,即使经过RLHF对齐的模型也难以完全抵抗精心设计的间接提示词注入71% similarUnverified2024年以来,研究者发现了对抗性后缀攻击(Adversarial Suffix),即在正常提示后附加一串看似无意义的token序列就能让模型忽略安全对齐68% similarUnverifiedAdversarial perturbation tools are still at an early stage, and their defensive effectiveness varies across different models.67% similarUnverifiedSecurity researchers are concerned that as large language models become more deeply integrated with penetration testing frameworks, the barrier to agentic attacks will continue to lower while execution speed and decision-making quality will continue to improve.65% similar
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