AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Video Multimodal Large Language Models (Video-MLLMs) support reasoning over video inputs, yet remain vulnerable to jailbreak attacks that elicit policy-violating responses. Existing video jailbreaks primarily manipulate how harmful queries are visually presented, thereby treating video merely as a carrier. Consequently...
PipePoison is presented, which collects fine-grained stage feedback from local shadow systems, uses chain-structured losses to identify and optimize the stage bottlenecking end-to-end success, and applies stability-calibrated stage and configuration weights to improve transferability.
It is shown that feedback-based agents can retain early biases even when later correction is available, and a black-box framework for exploiting this weakness is proposed that operationalizes the three factors as directional-shift, contextual-plausibility, and counterevidence-resilience signals under either limited tar...
This paper proposes ToolSiphon, a query-only extraction attack that introduces two complementary signals: a target-discriminative signal, implemented through Tool Contrastive Analysis, to steer queries toward the target tool; and a response-grounded factual signal, implemented through Evidence Chained Feedback, to miti...
Control evaluations reveal that targeted poisoning risk varies across memory operations and motivate stage-aware evaluation and control of LLM-agent memory, showing that targeted poisoning risk varies across memory operations.
The first extraction attack designed for this threat model, SPORE decouples the adversarial command from retrieval anchors by persisting the command in short-term memory and emitting semantically pure anchors in tool responses, demonstrating that memory isolation alone is insufficient and call for reexamining tool-side...
Scaling Large Language Models (LLMs) has been driven mainly by enlarging the Transformer backbone, but for an already-strong model this requires another round of costly pretraining. We study whether an existing backbone can keep improving by allocating more computation to each token while leaving the Transformer backbo...
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