Spider-Sense: Intrinsic Risk Sensing for Efficient Agent Defense with Hierarchical Adaptive Screening
As large language models (LLMs) evolve into autonomous agents, their real-world applicability has expanded significantly, accompanied by new security challenges. Most existing agent defense mechanisms adopt a mandatory checking paradigm, in which security validation is forcibly triggered at predefined stages of the agent lifecycle. In this work, we argue that effective agent security should be intrinsic and selective rather than architecturally decoupled and mandatory. We propose Spider-Sense framework, an event-driven defense framework based on Intrinsic Risk Sensing (IRS), which allows agents to maintain latent vigilance and trigger defenses only upon risk perception. Once triggered, the Spider-Sense invokes a hierarchical defence mechanism that trades off efficiency and precision: it resolves known patterns via lightweight similarity matching while escalating ambiguous cases to deep internal reasoning, thereby eliminating reliance on external models. To facilitate rigorous evaluation, we introduce S^2Bench, a lifecycle-aware benchmark featuring realistic tool execution and multi-stage attacks. Extensive experiments demonstrate that Spider-Sense achieves competitive or superior defense performance, attaining the lowest Attack Success Rate (ASR) and False Positive Rate (FPR), with only a marginal latency overhead of 8.3\%.
698599b14ad556f294b7ecdc
Zhenxiong Yu
698599b14ad556f294b7ecdd
Zhi Yang
claimed_verified
2026-02-06T18:50:37.407Z
64aa645404e7b379feccc490
https://cdn-avatars.huggingface.co/v1/production/uploads/64aa645404e7b379feccc490/4m8qcdy2OGK8visR5Jjl5.png
Zhi Yang
yangzhi1
user
yangzhi1
698599b14ad556f294b7ecde
Zhiheng Jin
698599b14ad556f294b7ecdf
Shuhe Wang
698599b14ad556f294b7ece0
Heng Zhang
698599b14ad556f294b7ece1
Yanlin Fei
698599b14ad556f294b7ece2
Lingfeng Zeng
698599b14ad556f294b7ece3
Fangqi Lou
698599b14ad556f294b7ece4
Shuo Zhang
698599b14ad556f294b7ece5
Tu Hu
698599b14ad556f294b7ece6
Jingping Liu
698599b14ad556f294b7ece7
Rongze Chen
698599b14ad556f294b7ece8
Xingyu Zhu
698599b14ad556f294b7ece9
Kunyi Wang
698599b14ad556f294b7ecea
Chaofa Yuan
claimed_verified
2026-04-01T08:37:01.135Z
636249a34c9f829ef7f7c710
https://cdn-avatars.huggingface.co/v1/production/uploads/636249a34c9f829ef7f7c710/M7DF88ByGAAXwARjEy29l.jpeg
bbruceyuan
bbruceyuan
user
bbruceyuan
698599b14ad556f294b7eceb
Xin Guo
698599b14ad556f294b7ecec
Zhaowei Liu
698599b14ad556f294b7eced
Feipeng Zhang
698599b14ad556f294b7ecee
Jie Huang
698599b14ad556f294b7ecef
Huacan Wang
698599b14ad556f294b7ecf0
Ronghao Chen
698599b14ad556f294b7ecf1
Liwen Zhang
Spider-Sense: Intrinsic Risk Sensing for Efficient Agent Defense with Hierarchical Adaptive Screening
As large language models (LLMs) evolve into autonomous agents, their real-world applicability has expanded significantly, accompanied by new security challenges. Most existing agent defense mechanisms adopt a mandatory checking paradigm, in which security validation is forcibly triggered at predefined stages of the agent lifecycle. In this work, we argue that effective agent security should be intrinsic and selective rather than architecturally decoupled and mandatory. We propose Spider-Sense framework, an event-driven defense framework based on Intrinsic Risk Sensing (IRS), which allows agents to maintain latent vigilance and trigger defenses only upon risk perception. Once triggered, the Spider-Sense invokes a hierarchical defence mechanism that trades off efficiency and precision: it resolves known patterns via lightweight similarity matching while escalating ambiguous cases to deep internal reasoning, thereby eliminating reliance on external models. To facilitate rigorous evaluation, we introduce S^2Bench, a lifecycle-aware benchmark featuring realistic tool execution and multi-stage attacks. Extensive experiments demonstrate that Spider-Sense achieves competitive or superior defense performance, attaining the lowest Attack Success Rate (ASR) and False Positive Rate (FPR), with only a marginal latency overhead of 8.3\%.
Spider-Sense framework provides intrinsic and selective agent security through event-driven defense with intrinsic risk sensing, achieving low attack success and false positive rates with minimal latency overhead.
large language models autonomous agents security challenges mandatory checking paradigm event-driven defense Intrinsic Risk Sensing hierarchical defense mechanism lightweight similarity matching deep internal reasoning lifecycle-aware benchmark Attack Success Rate False Positive Rate