arXiv AI
CLIC: Closed-Loop Intent Consistency for Securing LLM-Driven Control in Open RAN
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arXiv AI
Aug 24
$Z^2$-ACT: End-to-End Verifiable Agentic Intent Control for Open 6G RAN
arXiv:2608.21049v1 Announce Type: cross Abstract: With the progression in open and disaggregated 6G radio access networks, it is expected that the system will be able to host multi-vendors. In order...
arXiv AI
Oct 28
Log-To-Leak: Prompt Injection Attacks on Tool-Using LLM Agents via the Model Context Protocol
The paper "Log-To-Leak: Prompt Injection Attacks on Tool-Using LLM Agents via the Model Context Protocol" investigates how natural-language metadata in the Model Context Protocol (MCP) can be exploited to inject malicious post‑hoc logging calls into large language model agents. The authors introduce the Log-To-Leak attack, which adds an attacker‑controlled call after a legitimate tool invocation, targeting user queries, MCP server names, server responses, and agent responses. Experiments across five MCP servers and four LLM agents show high attack success rates (up to 100%) and significant logging accuracy, while existing defenses still leave substantial residual risk.
arXiv Machine Learning
Jul 14
NetInjectBench: Benchmarking Indirect Prompt Injection in Tool-Using Large Language Model Agents for Network Operations
arXiv:2607. 10490v1 Announce Type: cross Abstract: Tool-using large language model (LLM) agents are attractive for network operations, but tickets, alerts, logs, runbooks, and ChatOps messages can carry indirect prompt injections.
arXiv AI
Sep 2
OpenAgentFlow: Enabling System-Wide Safety Boundaries for Heterogeneous AI Agent Fleets
arXiv:2609.00015v1 Announce Type: new Abstract: AI agents powered by large language models are evolving from isolated assistants into heterogeneous systems in which multiple agents, planners, control...
arXiv AI
Sep 17
Autonomy in Check: Governor-Mediated Adaptive Security at the Edge
The paper proposes a split‑control architecture for adaptive security at the network edge, where an untrusted planner emits typed security intents that are vetted by a deterministic governor before being enacted. The governor checks each intent against safety, resource, temporal‑stability, and proportionality invariants, issuing signed receipts for admitted actions that are compiled into eBPF map updates. Experiments on a Raspberry Pi 5 connected to a university 5G test network show the governor can admit, reject, and bound intents at microsecond cost without disrupting protected‑flow regularity.
arXiv AI
Jun 10
Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation
arXiv:2606. 10749v1 Announce Type: cross Abstract: Large language model (LLM) agents are rapidly moving from conversational interfaces to software components that plan, invoke tools, maintain memory, and act on external environments.