Vertical Federated Learning (VFL) enables participants to collaboratively train models on aligned samples while keeping their heterogeneous features private and distributed. Despite their utility, VFL models remain vulnerable to adversarial attacks during inference. Adversarial Training (AT), which generates adversarial examples at each training iteration, stands as the most effective defense for improving model robustness. However, applying AT in VFL settings (VFAL) faces significant computational efficiency challenges, as the distributed training framework necessitates iterative propagations across participants. To this end, we propose **_DecVFAL_** framework, which substantially accelerates **_VFAL_** training through a dual-level ***Dec***oupling mechanism applied during adversarial sample generation. Specifically, we first decouple the bottom modules of clients (directly responsible for adversarial updates) from the remaining networks, enabling efficient _lazy sequential propagations_ that reduce communication frequency through delayed gradients. We further introduce _decoupled parallel backpropagation_ to accelerate delayed gradient computation by eliminating idle waiting through parallel processing across modules. Additionally, we are the first to establish convergence analysis for VFAL, rigorously characterizing how our decoupling mechanism interacts with existing VFL dynamics, and prove that _DecVFAL_ achieves an $\mathcal{O}(1/\sqrt{K})$ convergence rate matching that of standard VFLs. Experimental results show that _DecVFAL_ ensures competitive robustness while significantly achieving about $3\sim10\times$ speed up.

Coverage-Guided Fuzzing (CGF) has become the most popular and effective method for vulnerability detection. It is usually designed as an automated “black-box” tool. Security auditors start it and then just wait for the results. However, after a period of testing, CGF struggles to find new coverage gradually, thus making it inefficient. It is difficult for users to explain reasons that prevent fuzzing from making further progress and to determine whether the existing coverage is sufficient. In addition, there is no way to interact and direct the fuzzing process. In this paper, we design the dynamic directed greybox fuzzing (DDGF) to facilitate collaboration between the user and fuzzer. By leveraging Ball-Larus path profiling algorithm, we propose two new techniques: dynamic introspection and dynamic direction. Dynamic introspection reveals the significant imbalance in the distribution of path frequency through encoding and decoding. Based on the insight from introspection, users can dynamically direct the fuzzer to focus testing on the selected paths in real time. We implement DDGF based on AFL++. Experiments on Magma show that DDGF is effective in helping the fuzzer to reproduce vulnerabilities faster, with up to 100x speedup and only 13% performance overhead. DDGF shows the great potential of human-in-the-loop for fuzzing.

Intel SGX provides protected memory called enclave to secure the private user data against corrupted or malicious OS environment. However, several researches have shown that the SGX applications suffer from memory corruption vulnerabilities, thus leading to critical information leakage. Detecting memory corruption vulnerability in SGX applications can be cumbersome. Existing works either use symbolic execution or formal methods to analyze the enclave library, which is known to be inefficient and errors prone. Fuzzing, an effective and efficient vulnerability detection method is rarely used in SGX and has limitations.

We study the downlink beamformer design problem considering different real-time requirements of users. The problem is formulated mathematically as a non-convex optimization problem. The objective is to maximize the minimum signal to interference plus noise ratio (SINR) of the non-real-time service users, considering the SINR constraints of the real-time users and the total transmission power constraint. We carefully study the feasibility conditions for the original non-convex optimization problem. The results provide both theoretical and physical insights into the beamformer design problem. After a series of equivalence and relaxation of the original problem, the solution can be found via bisection feasibility checking and solving a set of geometric programming (GP) problems obtained from randomization. Combining the feasibility checking algorithm and the randomization procedure, we have our algorithm for computing the beamforming vector. Numerical results are provided for illustration.