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GitHub Next introduces enclaves, an experimental security primitive for GitHub Agentic Workflows that lets agents work across repositories while deterministically constraining what they can communicate back, mitigating private-to-public data leaks like the GitLost attack.
AgentGFM proposes a graph foundation model where each node acts as an agent with a shared trainable policy, enabling adaptive information-flow control through a predict–act–observe–correct process. It achieves strong performance across node-level, graph-level, and large-scale transfer tasks.
This paper presents ActPlane, a policy engine that enforces safety and effectiveness policies for AI agents at the OS kernel level using eBPF, bridging the semantic gap between natural language policy intent and concrete system actions.
This paper identifies a structural failure in multi-agent AI pipelines where memory-layer attacks can be misattributed as model misalignment, formalizing Semantic Norm Drift (SND) and proposing Counterfactual Composition Testing and Memory-Persistent Information-Flow Control as defenses.