Large Language Model (LLM)-based Multi-Agent Systems (MAS) are vulnerable to semantic attacks that can trigger cascading failures across the network. Current defenses face a fundamental dilemma: lightweight single-auditor methods are prone to single points of failure, while robust committee-based approaches (e.g., voting) incur prohibitive computational costs in multi-turn interactions. To resolve this tension, we propose MAS-Shield, a secure and efficient defense framework designed with a coarse-to-fine filtering pipeline. Rather than applying uniform scrutiny, MAS-Shield dynamically allocates resources by first identifying high-influence nodes via Critical Agent Auditing Selection. These nodes undergo Light Auditing using lightweight sentry models to rapidly filter benign cases, escalating only high-risk signals to a heavyweight Two-Round Consensus mechanism. This hierarchical design optimizes the security-efficiency trade-off. Experiments demonstrate that MAS-Shield achieves a 92.5% recovery rate against diverse adversarial scenarios and reduces defense latency by over 70% compared to existing methods.

Agentic workflows, composed of multiple collaborating Large Language Models (LLMs), have become a key paradigm for complex problem-solving. However, their effectiveness is often hindered by three critical challenges: high manual design costs, inefficient agentic search, and poor dynamic adaptability to new tasks and human preferences. To address these limitations, we propose HFlow, an evolutionary framework for generating agentic workflows through human-agent collaboration. HFlow employs an evolutionary algorithm to automate the search for optimal workflows by mutating and crossing over their structures, prompts, and LLM backbones. This process is guided by human preferences to ensure rapid convergence, while a hierarchical experience memory enables the generalization of learned strategies. Extensive experiments on math and code generation benchmarks show HFlow surpasses other automated baselines by up to 27.34%, while achieving comparable performance to o1-preview at only one-fourth of the cost. Our work introduces a new paradigm for workflow design that produces cost-effective and adaptive solutions, better aligning automated agentic systems with dynamic human needs.

Distributed agents need to exchange textual evidence across organizational boundaries, but these exchanges can expose two types of private information. One is carried by explicit identifiers. The other appears as source signatures spread across formatting, vocabulary, and syntax. Existing PII detection and masking methods mainly handle localized identifiers, leaving these broader source signals largely intact. Even masking 19.2% of tokens reduces TF-IDF stylometric attribution by only 18.6%. To address this problem, we propose *DiSan*, which treats text sanitization as a representation learning problem. A two-stream encoder maps text into orthogonal role and style subspaces. The role subspace captures task semantics shared across sources, while the local style subspace captures variation associated with source identity. The style representation is never transmitted directly. Prototype alignment supports joint training without centralizing raw text, while adversarial regularization limits source leakage in shared representations and prototypes. We test leakage in sanitized text, learned representations, and uploaded prototypes. On a distributed multi-agent RAG benchmark, *DiSan* reduces PII exposure in answers by 20× while preserving 83% answer faithfulness. On Enron emails, it reduces TF-IDF stylometric attribution by 73.2%, compared with 18.6% for the strongest masking baseline. Embedding and prototype attribution are also close to random. Together, these results show that *DiSan* reduces explicit and distributional leakage while retaining task utility.