Yuzhou Nie

Saastha Vasan, Yuzhou Nie, Kaie Chen et al.

Automated malware analysis increasingly relies on machine learning, yet most existing methods remain task-specific and depend on handcrafted features or narrowly scoped models. Recent developments in binary-level foundation models suggest a path toward reusable program representations, but their application to malware analysis remains underexplored, and most still operate at byte-level tokenization, limiting their ability to capture multi-byte code patterns. In this work, we introduce MalwarePT, a binary-level foundation model for malware analysis built on a ModernBERT-style encoder and pretrained with masked language modeling on Windows PE code-section bytes. We study whether a single pretrained encoder can transfer across malware-analysis tasks at different granularities, and how tokenization design affects that transfer. We train a byte-pair encoding (BPE) tokenizer on code-section bytes to compress frequent multi-byte patterns within a fixed context budget. We evaluate MalwarePT on three downstream tasks spanning token-, function-, and document-level prediction: API call prediction, functionality classification, and malware (program) detection under temporal drift. Our evaluation demonstrates that pretraining yields substantial gains for API call prediction and functionality classification, and that increasing the BPE vocabulary beyond the byte-level baseline improves performance, with the strongest overall tradeoff at a vocabulary size of 1,024 tokens. In malware detection at FPR ~ 0.001, MalwarePT outperforms the neural network baselines, and is complementary to feature-engineering models that rely on PE structure. We also compare against existing binary foundation models and show that MalwarePT's design choices yield gains across all downstream tasks.

Zhaorun Chen, Xun Liu, Haibo Tong et al.

AI agents are increasingly deployed across diverse domains to automate complex workflows through long-horizon and high-stakes action executions. Due to their high capability and flexibility, such agents raise significant security and safety concerns. A growing number of real-world incidents have shown that adversaries can easily manipulate agents into performing harmful actions, such as leaking API keys, deleting user data, or initiating unauthorized transactions. Evaluating agent security is inherently challenging, as agents operate in dynamic, untrusted environments involving external tools, heterogeneous data sources, and frequent user interactions. However, realistic, controllable, and reproducible environments for large-scale risk assessment remain largely underexplored. To address this gap, we introduce the DecodingTrust-Agent Platform (DTap), the first controllable and interactive red-teaming platform for AI agents, spanning 14 real-world domains and over 50 simulation environments that replicate widely used systems such as Google Workspace, Paypal, and Slack. To scale the risk assessment of agents in DTap, we further propose DTap-Red, the first autonomous red-teaming agent that systematically explores diverse injection vectors (e.g., prompt, tool, skill, environment, combinations) and autonomously discovers effective attack strategies tailored to varying malicious goals. Using DTap-Red, we curate DTap-Bench, a large-scale red-teaming dataset comprising high-quality instances across domains, each paired with a verifiable judge to automatically validate attack outcomes. Through DTap, we conduct large-scale evaluations of popular AI agents built on various backbone models, spanning security policies, risk categories, and attack strategies, revealing systematic vulnerability patterns and providing valuable insights for developing secure next-generation agents.

Zhun Wang, Vincent Siu, Zhe Ye et al. · berkeley

The strong planning and reasoning capabilities of Large Language Models (LLMs) have fostered the development of agent-based systems capable of leveraging external tools and interacting with increasingly complex environments. However, these powerful features also introduce a critical security risk: indirect prompt injection, a sophisticated attack vector that compromises the core of these agents, the LLM, by manipulating contextual information rather than direct user prompts. In this work, we propose a generic black-box fuzzing framework, AgentVigil, designed to automatically discover and exploit indirect prompt injection vulnerabilities across diverse LLM agents. Our approach starts by constructing a high-quality initial seed corpus, then employs a seed selection algorithm based on Monte Carlo Tree Search (MCTS) to iteratively refine inputs, thereby maximizing the likelihood of uncovering agent weaknesses. We evaluate AgentVigil on two public benchmarks, AgentDojo and VWA-adv, where it achieves 71% and 70% success rates against agents based on o3-mini and GPT-4o, respectively, nearly doubling the performance of baseline attacks. Moreover, AgentVigil exhibits strong transferability across unseen tasks and internal LLMs, as well as promising results against defenses. Beyond benchmark evaluations, we apply our attacks in real-world environments, successfully misleading agents to navigate to arbitrary URLs, including malicious sites.