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.

Detection of Compromised Functions in a Serverless Cloud Environment

Serverless computing is an emerging cloud paradigm with serverless functions at its core. While serverless environments enable software developers to focus on developing applications without the need to actively manage the underlying runtime infrastructure, they open the door to a wide variety of security threats that can be challenging to mitigate with existing methods. Existing security solutions do not apply to all serverless architectures, since they require significant modifications to the serverless infrastructure or rely on third-party services for the collection of more detailed data. In this paper, we present an extendable serverless security threat detection model that leverages cloud providers' native monitoring tools to detect anomalous behavior in serverless applications. Our model aims to detect compromised serverless functions by identifying post-exploitation abnormal behavior related to different types of attacks on serverless functions, and therefore, it is a last line of defense. Our approach is not tied to any specific serverless application, is agnostic to the type of threats, and is adaptable through model adjustments. To evaluate our model's performance, we developed a serverless cybersecurity testbed in an AWS cloud environment, which includes two different serverless applications and simulates a variety of attack scenarios that cover the main security threats faced by serverless functions. Our evaluation demonstrates our model's ability to detect all implemented attacks while maintaining a negligible false alarm rate.

Adversarial Machine Learning Threat Analysis in Open Radio Access Networks

The Open Radio Access Network (O-RAN) is a new, open, adaptive, and intelligent RAN architecture. Motivated by the success of artificial intelligence in other domains, O-RAN strives to leverage machine learning (ML) to automatically and efficiently manage network resources in diverse use cases such as traffic steering, quality of experience prediction, and anomaly detection. Unfortunately, ML-based systems are not free of vulnerabilities; specifically, they suffer from a special type of logical vulnerabilities that stem from the inherent limitations of the learning algorithms. To exploit these vulnerabilities, an adversary can utilize an attack technique referred to as adversarial machine learning (AML). These special type of attacks has already been demonstrated in recent researches. In this paper, we present a systematic AML threat analysis for the O-RAN. We start by reviewing relevant ML use cases and analyzing the different ML workflow deployment scenarios in O-RAN. Then, we define the threat model, identifying potential adversaries, enumerating their adversarial capabilities, and analyzing their main goals. Finally, we explore the various AML threats in the O-RAN and review a large number of attacks that can be performed to materialize these threats and demonstrate an AML attack on a traffic steering model.