Oleg Brodt

Kalanit Suzan Segal, Hadar Cochavi Gorelik, Oleg Brodt et al.

Modern computing systems rely on the Unified Extensible Firmware Interface (UEFI), which has replaced the legacy Basic Input/Output System (BIOS) as the firmware standard for the modern boot process. Although the UEFI represents a significant advancement in system firmware, it is increasingly targeted by threat actors seeking to exploit its execution environment and take advantage of its persistence mechanisms. While some security-related analysis of UEFI components has been performed--primarily via debugging and runtime behavior testing--to the best of our knowledge, no prior study has specifically addressed the capturing and analysis of volatile UEFI runtime memory to detect malicious exploitation during the pre-OS phase. This gap in UEFI forensic tools limits the ability to conduct in-depth security analysis in pre-OS environments. Such a gap is particularly surprising, given that memory forensics is widely regarded as foundational to modern incident response, as reflected by the popularity of above-OS memory analysis frameworks, such as Rekall, Volatility, and MemProcFS. To address the lack of below-OS memory forensics, we introduce a framework for UEFI memory forensics. The proposed framework consists of two components: UEFIMemDump, a memory acquisition tool, and UEFIDumpAnalysis, an extendable collection of analysis modules capable of detecting malicious activities such as function pointer hooking, inline hooking, malicious image loading, and gadget-based control-flow manipulation. Our proof-of-concept implementation demonstrates the framework's ability to detect modern UEFI threats, such as Thunderstrike, CosmicStrand, and Glupteba bootkits. By providing an open-source solution, our work enables researchers and practitioners to investigate firmware-level threats, develop additional analysis modules, and advance overall below-OS security through UEFI memory analysis.

Danielle Lavi, Oleg Brodt, Dudu Mimran et al.

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.

Ben Nassi, Bruce Schneier, Oleg Brodt

The rapid adoption of large language model (LLM)-based systems -- from chatbots to autonomous agents capable of executing code and financial transactions -- has created a new attack surface that existing security frameworks inadequately address. The dominant framing of these threats as "prompt injection" -- a catch-all phrase for security failures in LLM-based systems -- obscures a more complex reality: Attacks on LLM-based systems increasingly involve multi-step sequences that mirror traditional malware campaigns. In this paper, we propose that attacks targeting LLM-based applications constitute a distinct class of malware, which we term \textit{promptware}, and introduce a five-step kill chain model for analyzing these threats. The framework comprises Initial Access (prompt injection), Privilege Escalation (jailbreaking), Persistence (memory and retrieval poisoning), Lateral Movement (cross-system and cross-user propagation), and Actions on Objective (ranging from data exfiltration to unauthorized transactions). By mapping recent attacks to this structure, we demonstrate that LLM-related attacks follow systematic sequences analogous to traditional malware campaigns. The promptware kill chain offers security practitioners a structured methodology for threat modeling and provides a common vocabulary for researchers across AI safety and cybersecurity to address a rapidly evolving threat landscape.

Edan Habler, Ron Bitton, Dan Avraham et al.

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, it has been shown that ML-based systems are vulnerable to an attack technique referred to as adversarial machine learning (AML). This special kind of attack has already been demonstrated in recent studies and in multiple domains. In this paper, we present a systematic AML threat analysis for 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. Next, we explore the various AML threats associated with O-RAN and review a large number of attacks that can be performed to realize these threats and demonstrate an AML attack on a traffic steering model. In addition, we analyze and propose various AML countermeasures for mitigating the identified threats. Finally, based on the identified AML threats and countermeasures, we present a methodology and a tool for performing risk assessment for AML attacks for a specific ML use case in O-RAN.

Dudu Mimran, Ron Bitton, Yehonatan Kfir et al.

The Open Radio Access Network (O-RAN) is a promising RAN architecture, aimed at reshaping the RAN industry toward an open, adaptive, and intelligent RAN. In this paper, we conducted a comprehensive security analysis of Open Radio Access Networks (O-RAN). Specifically, we review the architectural blueprint designed by the O-RAN alliance -- A leading force in the cellular ecosystem. Within the security analysis, we provide a detailed overview of the O-RAN architecture; present an ontology for evaluating the security of a system, which is currently at an early development stage; detect the primary risk areas to O-RAN; enumerate the various threat actors to O-RAN; and model potential threats to O-RAN. The significance of this work is providing an updated attack surface to cellular network operators. Based on the attack surface, cellular network operators can carefully deploy the appropriate countermeasure for increasing the security of O-RAN.

Asaf Nadler, Ron Bitton, Oleg Brodt et al.

Anti-malware agents typically communicate with their remote services to share information about suspicious files. These remote services use their up-to-date information and global context (view) to help classify the files and instruct their agents to take a predetermined action (e.g., delete or quarantine). In this study, we provide a security analysis of a specific form of communication between anti-malware agents and their services, which takes place entirely over the insecure DNS protocol. These services, which we denote DNS anti-malware list (DNSAML) services, affect the classification of files scanned by anti-malware agents, therefore potentially putting their consumers at risk due to known integrity and confidentiality flaws of the DNS protocol. By analyzing a large-scale DNS traffic dataset made available to the authors by a well-known CDN provider, we identify anti-malware solutions that seem to make use of DNSAML services. We found that these solutions, deployed on almost three million machines worldwide, exchange hundreds of millions of DNS requests daily. These requests are carrying sensitive file scan information, oftentimes - as we demonstrate - without any additional safeguards to compensate for the insecurities of the DNS protocol. As a result, these anti-malware solutions that use DNSAML are made vulnerable to DNS attacks. For instance, an attacker capable of tampering with DNS queries, gains the ability to alter the classification of scanned files, without presence on the scanning machine. We showcase three attacks applicable to at least three anti-malware solutions that could result in the disclosure of sensitive information and improper behavior of the anti-malware agent, such as ignoring detected threats. Finally, we propose and review a set of countermeasures for anti-malware solution providers to prevent the attacks stemming from the use of DNSAML services.