AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Multi-turn attacks on agentic systems can compose individually permissible actions into harmful outcomes, challenging defenses that assess actions or states in isolation. We show that such attacks leave a detectable signature in the agent's internal representations: harmful behavior emerges as an accumulated representa...
This work formalizes influence as a counterfactual estimand, distinguishes specification mismatch across estimands from approximation error in estimating a fixed estimand, and organizes representative estimators by their implied specifications.
A training-free defense that first identifies safety-specific neurons through per-neuron hypothesis tests under false-discovery-rate control together with a utility-specificity filter, and reduces the average attack success rate to at most 2.0% while incurring a utility drop of only 0.3% on MT-Bench, the smallest among...
As large language models are increasingly deployed in real-world systems, safety failures can still lead to harmful outputs and dangerous misuse. We argue that the essence of safety is adversarial: many failures arise not from natural inputs alone, but from strategic attempts to evade model policies and safeguards. How...
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