This paper addresses the dual challenge of security and robustness in collaborative decision-making for multi-UAV systems operating in dynamic and adversarial environments， where traditional approaches that decouple safety mechanisms from control policies often fail under anomalies. To this end， we propose adaptive security control with adversarial-resilient endogenous strategy （ASC-ARES）， a novel framework grounded in “security by design” and “security left shift” principles that systematically embeds multi-layer constraints， including biconnected topology control， physical collision avoidance， and energy management， into deep reinforcement learning via structured state modeling and reward shaping. Methodologically， ASC-ARES extends the deep deterministic policy gradient （DDPG） algorithm to handle hybrid action spaces through a dual-head policy network for joint optimization of three-dimensional continuous attitude and discrete yaw actions. It further integrates a centroid-guided biconnectivity control algorithm to enable proactive network connectivity awareness and constructs a mean opinion score （MOS）- driven multi-objective adaptive reward mechanism to synergistically optimize quality of experience （QoE）， network resilience， safety， and energy efficiency. Experimental results demonstrate that ASC-ARES achieves superior convergence and stability， maintaining an MOS fluctuation rate of only 0.36% and a biconnectivity success rate of 99.98%. Under fast gradient sign method （FGSM）， projected gradient descent （PGD）， and strong noise interference （?=2.0）， the system exhibits exceptional topology reconstruction and state recovery capabilities， with an average performance restoration rate exceeding 80% after interference removal. Ablation studies confirm that the topology control module improves service quality by 59%， while the repulsion mechanism reduces collision risk by 85%. These findings establish ASC-ARES as an effective paradigm for achieving integrated performance-security co-optimization in resource-constrained multi-agent systems.