The increasing reliance on third-party intellectual property (IP) cores in modern integrated circuit (IC) design has introduced significant security vulnerabilities, particularly the risk of Hardware Trojans (HTs). These malicious modifications can compromise system integrity, leading to data leakage, unauthorized access, or functional failures. Traditional detection methods often depend on the availability of a golden chip, which is not always feasible. This paper presents the 2025 ICCAD CAD Contest Problem A, which challenges participants to develop machine learning-based solutions for detecting HTs directly from gate-level netlists without requiring a golden reference. The problem is formulated with defined Trojan behaviors, input/output specifications, and evaluation metrics, including correctness and F1 score. The contest aims to foster innovation in HT detection by leveraging advanced data-driven techniques and scalable analysis frameworks.

Large Language Models (LLMs) are increasingly central to agentic systems due to their strong reasoning and planning capabilities. By interacting with external environments through predefined tools, these agents can carry out complex user tasks. Nonetheless, this interaction also introduces the risk of prompt injection attacks, where malicious inputs from external sources can mislead the agent’s behavior, potentially resulting in economic loss, privacy leakage, or system compromise. System-level defenses have recently shown promise by enforcing static or predefined policies, but they still face two key challenges: the ability to dynamically update security rules and the need for memory stream isolation. To address these challenges, we propose DRIFT, a Dynamic Rule-based Isolation Framework for Trustworthy agentic systems, which enforces both control- and data-level constraints. A Secure Planner first constructs a minimal function trajectory and a JSON-schema-style parameter checklist for each function node based on the user query. A Dynamic Validator then monitors deviations from the original plan, assessing whether changes comply with privilege limitations and the user's intent. Finally, an \textit{Injection Isolator} detects and masks any instructions that may conflict with the user query from the memory stream to mitigate long-term risks. We empirically validate the effectiveness of DRIFT on the AgentDojo and ASB benchmark, demonstrating its strong security performance while maintaining high utility across diverse models—showcasing both its robustness and adaptability. The code is released at https://github.com/SaFoLab-WISC/DRIFT.

Modern directed fuzzing often faces scalability issues when analyzing multiple targets in a program simultaneously. We observe that the root cause is that directed fuzzers are unaware of the correlations among the targets, thereby could degenerate into a target-undirected method. As a result, directed fuzzing suffers severely from efficiency when reproducing multiple targets.This paper presents Titan, which enables fuzzers to distinguish correlations among various targets in the program and, thus, optimizes the input generation to reproduce multiple targets effectively. Leveraging these correlations, Titan differentiates seeds’ potential of reaching each target for the scheduling and identifies bytes that can be changed simultaneously for the mutation. We compare our approach to eight state-of-the-art (directed) fuzzers. The evaluation demonstrates that Titan outperforms existing approaches by efficiently detecting multiple targets, achieving a 21.4x speedup and requiring 95.0% fewer number of executions. In addition, Titan detects nine incomplete fixes, which cannot be detected by other directed fuzzers, in the latest versions of the benchmark programs with two CVE IDs assigned.

Scheduling seeds, i.e., selecting seed for mutation from a pool of candidates, significantly impacts the speed of a greybox fuzzer to achieve a target coverage rate. Despite much progress in improving seed scheduling, existing work cannot escape from the high-cost trap or the high-benefit trap: one line of approaches believes high cost implies high benefit and, thus, prefers the seeds that explore infrequently-visited paths; the other type of approach directly calculates the potential benefits, e.g., the number of blocks able to cover, and prefers high-benefit seeds. Due to the ignorance of the impacts of either the cost or the benefits, they often trap fuzzers into mutating the seeds without increasing coverage. This paper presents BELIEFFUZZ, which transforms fuzzing into a Monte Carlo planning with upper confidence bound system. The system allows us to dynamically compute both the benefits and the cost during the fuzzing process. The experimental results demonstrated that our approach achieves a significant efficiency improvement, with 2.12x-5.63x speedups and 1.18x-2.77x fewer executions needed, over the state of the art to achieve the same coverage. Moreover, BELIEFFUZZ detected 31 more previously-unseen bugs in the real-world projects evaluated, with 18 CVEs assigned.