Minrong Chen

Jiasi Weng, Jian Weng, Minrong Chen et al.

The rapid adoption of artificial intelligence (AI)-driven and agentic workflows is transforming traditional government and enterprise systems into language-based, tool-using and increasingly autonomous infrastructures. Conventional key management services authenticate who may invoke a cryptographic primitive, but remain agnostic to which workflow steps are authorized at runtime: an authenticated agent can still be hijacked by direct or indirect prompt injection into executing malicious actions that satisfy identity checks yet violate user intent. We present \emph{Neural Cryptographic Services} (NCS), an active security governance plane grounded in a neural-symbolic design and interposed between LLM agents and privileged tools. Under NCS, an untrusted neural planner compiles natural-language instructions into structured plan drafts but holds no execution authority. Execution is instead gated by a deterministic symbolic controller operating over an offline-signed, hash-chained instruction stream. NCS verifies the signature, incrementally validates the hash chain, releases exactly one instruction payload at a time, and enforces strict binding between the agent's proposed tool parameters and the verified payload. Mismatched or out-of-order tool calls are rejected fail-closed, while previously verified states are retained for post-hoc auditing. We evaluate NCS on AgentDojo and a custom argument-hijacking benchmark. NCS drives attack success rates to near zero while preserving acceptable utility on benign workflows. NCS thus reframes agent security from asking whether a model's intent complied to asking whether a proposed dispatch matches a cryptographically authorized step.

GuangXian Gan, MinRong Chen

To address the issue that existing dynamic multi-objective optimization algorithms mainly rely on individual migration or independent special point sampling after environmental changes, while failing to fully exploit the structural relationships among representative solutions, a Special Point Skeleton Reconstruction based Dynamic Multi-Objective Evolutionary Algorithm (SPSR-DMOEA) is proposed. First, the centroid, knee points, and extreme points are extracted from the Pareto optimal solution set of the current environment, and their positions in the new environment are adaptively predicted according to their movement velocities across consecutive environments. Subsequently, in the decision space, the centroid is connected with other anchor points, and a minimum spanning tree is constructed among the non-centroid anchor points, thereby establishing a prediction skeleton capable of describing the overall population structure. According to the lengths of the skeleton edges, the number of individuals allocated to each edge is determined proportionally. Candidate solutions are uniformly generated along each edge, and random orthogonal perturbations are introduced to expand the search region around the skeleton. Experimental results on the DF dynamic multi-objective benchmark suite demonstrate the effectiveness of the proposed method in dynamic tracking capability.