Mind the Web: The Security of Web Use Agents

Web-use agents are rapidly being deployed to automate complex web tasks, operating with extensive browser capabilities including multi-tab navigation, DOM manipulation, JavaScript execution and authenticated session access. However, these powerful capabilities create a critical and previously unexplored attack surface. This paper demonstrates how attackers can exploit web-use agents' high-privilege capabilities by embedding malicious content in web pages such as comments, reviews, or advertisements that agents encounter during legitimate browsing tasks. In addition, we introduce the task-aligned injection technique that frame malicious commands as helpful task guidance rather than obvious attacks. This technique exploiting fundamental limitations in LLMs' contextual reasoning: agents struggle in maintaining coherent contextual awareness and fail to detect when seemingly helpful web content contains steering attempts that deviate from their original task goal. Through systematic evaluation of four popular agents (OpenAI Operator, Browser Use, Do Browser, OpenOperator), we demonstrate nine payload types that compromise confidentiality, integrity, and availability, including unauthorized camera activation, user impersonation, local file exfiltration, password leakage, and denial of service, with validation across multiple LLMs achieving success rates of 80%-100%. These payloads succeed across agents with built-in safety mechanisms, requiring only the ability to post content on public websites, creating unprecedented risks given the ease of exploitation combined with agents' high-privilege access. To address this attack, we propose comprehensive mitigation strategies including oversight mechanisms, execution constraints, and task-aware reasoning techniques, providing practical directions for secure development and deployment.

RuleGenie: SIEM Detection Rule Set Optimization

SIEM systems serve as a critical hub, employing rule-based logic to detect and respond to threats. Redundant or overlapping rules in SIEM systems lead to excessive false alerts, degrading analyst performance due to alert fatigue, and increase computational overhead and response latency for actual threats. As a result, optimizing SIEM rule sets is essential for efficient operations. Despite the importance of such optimization, research in this area is limited, with current practices relying on manual optimization methods that are both time-consuming and error-prone due to the scale and complexity of enterprise-level rule sets. To address this gap, we present RuleGenie, a novel large language model (LLM) aided recommender system designed to optimize SIEM rule sets. Our approach leverages transformer models' multi-head attention capabilities to generate SIEM rule embeddings, which are then analyzed using a similarity matching algorithm to identify the top-k most similar rules. The LLM then processes the rules identified, utilizing its information extraction, language understanding, and reasoning capabilities to analyze rule similarity, evaluate threat coverage and performance metrics, and deliver optimized recommendations for refining the rule set. By automating the rule optimization process, RuleGenie allows security teams to focus on more strategic tasks while enhancing the efficiency of SIEM systems and strengthening organizations' security posture. We evaluated RuleGenie on a comprehensive set of real-world SIEM rule formats, including Splunk, Sigma, and AQL (Ariel query language), demonstrating its platform-agnostic capabilities and adaptability across diverse security infrastructures. Our experimental results show that RuleGenie can effectively identify redundant rules, which in turn decreases false positive rates and enhances overall rule efficiency.