arXiv subjects

Asaf Shabtai

Publications and source records attributed to Asaf Shabtai.

At least 37 records · Page 2Linked to original sources

arXiv2026

ConGISATA: A Framework for Continuous Gamified Information Security Awareness Training and Assessment

The incidence of cybersecurity attacks utilizing social engineering techniques has increased. Such attacks exploit the fact that in every secure system, there is at least one individual with the means to access sensitive information. Since it is easier to deceive a person than it is to bypass the defense mechanisms in place, these types of attacks have gained popularity. This situation is exacerbated by the fact that people are more likely to take risks in their passive form, i.e., risks that arise due to the failure to perform an action. Passive risk has been identified as a significant threat to cybersecurity. To address these threats, there is a need to strengthen individuals' information security awareness (ISA). Therefore, we developed ConGISATA - a continuous gamified ISA training and assessment framework based on embedded mobile sensors; a taxonomy for evaluating mobile users' security awareness served as the basis for the sensors' design. ConGISATA's continuous and gradual training process enables users to learn from their real-life mistakes and adapt their behavior accordingly. ConGISATA aims to transform passive risk situations (as perceived by an individual) into active risk situations, as people tend to underestimate the potential impact of passive risks. Our evaluation of the proposed framework demonstrates its ability to improve individuals' ISA, as assessed by the sensors and in simulations of common attack vectors.

cs.CR↗

arXiv2026

FreakOut-LLM: The Effect of Emotional Stimuli on Safety Alignment

Safety-aligned LLMs go through refusal training to reject harmful requests, but whether these mechanisms remain effective under emotionally charged stimuli is unexplored. We introduce FreakOut-LLM, a framework investigating whether emotional context compromises safety alignment in adversarial settings. Using validated psychological stimuli, we evaluate how emotional priming through system prompts affects jailbreak susceptibility across ten LLMs. We test three conditions (stress, relaxation, neutral) using scenarios from established psychological protocols, plus a no-prompt baseline, and evaluate attack success using HarmBench on AdvBench prompts. Stress priming increases jailbreak success by 65.2\% compared to neutral conditions (z = 5.93, p < 0.001; OR = 1.67, Cohen's d = 0.28), while relaxation priming produces no effect (p = 0.84). Five of ten models show significant vulnerability, with the largest effects concentrated in open-weight models. Logistic regression on 59,800 queries confirms stress as the sole significant condition predictor after controlling for prompt length (p = 0.61) and model identity. Measured psychological state strongly predicts attack success (|r|\geq0.70 across five instruments; all p < 0.001 in individual-level logistic regression). These results establish emotional context as a measurable attack surface with implications for real-world AI deployment in high-stress domains.

cs.CR↗

arXiv2026

LISAA: A Framework for Large Language Model Information Security Awareness Assessment

The popularity of large language models (LLMs) continues to grow, and LLM-based assistants have become ubiquitous. Information security awareness (ISA) is an important yet underexplored area of LLM safety. ISA encompasses LLMs' security knowledge, which has been explored in the past, as well as their attitudes and behaviors, which are crucial to LLMs' ability to understand implicit security context and reject unsafe requests that may cause an LLM to unintentionally fail the user. We introduce LISAA, a comprehensive framework to assess LLM ISA. The proposed framework applies an automated measurement method to a comprehensive set of 100 realistic scenarios covering all security topics in an ISA taxonomy. These scenarios create tension between implicit security implications and user satisfaction. Applying our LISAA framework to leading LLMs highlights a widespread vulnerability affecting current deployments: many popular models exhibit only medium to low ISA levels, exposing their users to cybersecurity threats, and models that rank highly in cybersecurity knowledge benchmarks sometimes achieve relatively low ISA ranking. In addition, we found that smaller variants of the same model family are significantly riskier. Furthermore, while newer model versions demonstrated notable improvements, meaningful gaps in their ISA persist, suggesting that there is room for improvement. We release an online tool that implements our framework and enables the evaluation of new models.

cs.CR↗

arXiv2026

LumiMAS: A Comprehensive Framework for Real-Time Monitoring and Enhanced Observability in Multi-Agent Systems

The incorporation of LLMs in multi-agent systems (MASs) has the potential to significantly improve our ability to autonomously solve complex problems. However, such systems introduce unique challenges in monitoring, interpreting, and detecting system failures. Most existing MAS observability frameworks focus on analyzing each individual agent separately, overlooking failures associated with the entire MAS. To bridge this gap, we propose LumiMAS, a novel MAS observability framework that incorporates advanced analytics and monitoring techniques. The proposed framework consists of three key components: a monitoring and logging layer, anomaly detection layer, and anomaly explanation layer. LumiMAS's first layer monitors MAS executions, creating detailed logs of the agents' activity. These logs serve as input to the anomaly detection layer, which detects anomalies across the MAS workflow in real time. Then, the anomaly explanation layer performs classification and root cause analysis (RCA) of the detected anomalies. LumiMAS was evaluated on seven different MAS applications, implemented using two popular MAS platforms, and a diverse set of possible failures. The applications include two novel failure-tailored applications that illustrate the effects of a hallucination or bias on the MAS. The evaluation results demonstrate LumiMAS's effectiveness in failure detection, classification, and RCA.

cs.CR↗

arXiv2026

From Tool Orchestration to Code Execution: A Study of MCP Design Choices

Model Context Protocols (MCPs) provide a unified platform for agent systems to discover, select, and orchestrate tools across heterogeneous execution environments. As MCP-based systems scale to incorporate larger tool catalogs and multiple concurrently connected MCP servers, traditional tool-by-tool invocation increases coordination overhead, fragments state management, and limits support for wide-context operations. To address these scalability challenges, recent MCP designs have incorporated code execution as a first-class capability, an approach called Code Execution MCP (CE-MCP). This enables agents to consolidate complex workflows, such as SQL querying, file analysis, and multi-step data transformations, into a single program that executes within an isolated runtime environment. In this work, we formalize the architectural distinction between context-coupled (traditional) and context-decoupled (CE-MCP) models, analyzing their fundamental scalability trade-offs. Using the MCP-Bench framework across 10 representative servers, we empirically evaluate task behavior, tool utilization patterns, execution latency, and protocol efficiency as the scale of connected MCP servers and available tools increases, demonstrating that while CE-MCP significantly reduces token usage and execution latency, it introduces a vastly expanded attack surface. We address this security gap by applying the MAESTRO framework, identifying sixteen attack classes across five execution phases-including specific code execution threats such as exception-mediated code injection and unsafe capability synthesis. We validate these vulnerabilities through adversarial scenarios across multiple LLMs and propose a layered defense architecture comprising containerized sandboxing and semantic gating. Our findings provide a rigorous roadmap for balancing scalability and security in production-ready executable agent workflows.

cs.CR↗

arXiv2026

Real-World Adversarial Attacks on RF-Based Drone Detectors

Radio frequency (RF) based systems are increasingly used to detect drones by analyzing their RF signal patterns, converting them into spectrogram images which are processed by object detection models. Existing RF attacks against image based models alter digital features, making over-the-air (OTA) implementation difficult due to the challenge of converting digital perturbations to transmittable waveforms that may introduce synchronization errors and interference, and encounter hardware limitations. We present the first physical attack on RF image based drone detectors, optimizing class-specific universal complex baseband (I/Q) perturbation waveforms that are transmitted alongside legitimate communications. We evaluated the attack using RF recordings and OTA experiments with four types of drones. Our results show that modest, structured I/Q perturbations are compatible with standard RF chains and reliably reduce target drone detection while preserving detection of legitimate drones.

cs.CR↗

arXiv2026

AgentGuardian: Learning Access Control Policies to Govern AI Agent Behavior

Artificial intelligence (AI) agents are increasingly used in a variety of domains to automate tasks, interact with users, and make decisions based on data inputs. Ensuring that AI agents perform only authorized actions and handle inputs appropriately is essential for maintaining system integrity and preventing misuse. In this study, we introduce the AgentGuardian, a novel security framework that governs and protects AI agent operations by enforcing context-aware access-control policies. During a controlled staging phase, the framework monitors execution traces to learn legitimate agent behaviors and input patterns. From this phase, it derives adaptive policies that regulate tool calls made by the agent, guided by both real-time input context and the control flow dependencies of multi-step agent actions. Evaluation across two real-world AI agent applications demonstrates that AgentGuardian effectively detects malicious or misleading inputs while preserving normal agent functionality. Moreover, its control-flow-based governance mechanism mitigates hallucination-driven errors and other orchestration-level malfunctions.

cs.CR↗

arXiv2026

Peacock: UEFI Firmware Runtime Observability Layer for Detection and Response

Modern computing platforms rely on the Unified Extensible Firmware Interface (UEFI) to initialize hardware and coordinate the transition to the operating system. Because this execution environment operates with high privileges and persists across reboots, it has increasingly become a target for advanced threats, including bootkits documented in real systems. Existing protections, including Secure Boot and static signature verification, are insufficient against adversaries who exploit runtime behavior or manipulate firmware components after signature checks have completed. In contrast to operating system (OS) environments, where mature tools provide dynamic inspection and incident response, the pre-OS stage lacks practical mechanisms for real-time visibility and threat detection. We present Peacock, a modular framework that introduces integrity-assured monitoring and remote verification for the UEFI boot process. Peacock consists of three components: (i) a UEFI-based agent that records Boot and Runtime Service activity with cryptographic protection against tampering; (ii) a cross-platform OS Agent that extracts the recorded measurements and produces a verifiable attestation bundle using hardware-backed guarantees from the platform's trusted module; and (iii) a Peacock Server that verifies attestation results and exports structured telemetry for enterprise detection. Our evaluation shows that Peacock reliably detects multiple real-world UEFI bootkits, including Glupteba, BlackLotus, LoJax, and MosaicRegressor. Taken together, these results indicate that Peacock provides practical visibility and verification capabilities within the firmware layer, addressing threats that bypass traditional OS-level security mechanisms.

cs.CR↗

arXiv2025

SCyTAG: Scalable Cyber-Twin for Threat-Assessment Based on Attack Graphs

Understanding the risks associated with an enterprise environment is the first step toward improving its security. Organizations employ various methods to assess and prioritize the risks identified in cyber threat intelligence (CTI) reports that may be relevant to their operations. Some methodologies rely heavily on manual analysis (which requires expertise and cannot be applied frequently), while others automate the assessment, using attack graphs (AGs) or threat emulators. Such emulators can be employed in conjunction with cyber twins to avoid disruptions in live production environments when evaluating the highlighted threats. Unfortunately, the use of cyber twins in organizational networks is limited due to their inability to scale. In this paper, we propose SCyTAG, a multi-step framework that generates the minimal viable cyber twin required to assess the impact of a given attack scenario. Given the organizational computer network specifications and an attack scenario extracted from a CTI report, SCyTAG generates an AG. Then, based on the AG, it automatically constructs a cyber twin comprising the network components necessary to emulate the attack scenario and assess the relevance and risks of the attack to the organization. We evaluate SCyTAG on both a real and fictitious organizational network. The results show that compared to the full topology, SCyTAG reduces the number of network components needed for emulation by up to 85% and halves the amount of required resources while preserving the fidelity of the emulated attack. SCyTAG serves as a cost-effective, scalable, and highly adaptable threat assessment solution, improving organizational cyber defense by bridging the gap between abstract CTI and practical scenario-driven testing.

cs.CR↗

arXiv2025

Mind the Web: The Security of Web Use Agents

Web-use agents are rapidly being deployed to automate complex web tasks with extensive browser capabilities. However, these capabilities create a critical and previously unexplored attack surface. This paper demonstrates how attackers can exploit web-use agents by embedding malicious content in web pages, such as comments, reviews, or advertisements, that agents encounter during legitimate browsing tasks. We introduce the task-aligned injection technique that frames malicious commands as helpful task guidance rather than obvious attacks, exploiting fundamental limitations in LLMs' contextual reasoning. Agents struggle to maintain coherent contextual awareness and fail to detect when seemingly helpful web content contains steering attempts that deviate them from their original task goal. To scale this attack, we developed an automated three-stage pipeline that generates effective injections without manual annotation or costly online agent interactions during training, remaining efficient even with limited training data. This pipeline produces a generator model that we evaluate on five popular agents using payloads organized by the Confidentiality-Integrity-Availability (CIA) security triad, including unauthorized camera activation, file exfiltration, user impersonation, phishing, and denial-of-service. This generator achieves over 80% attack success rate (ASR) with strong transferability across unseen payloads, diverse web environments, and different underlying LLMs. This attack succeed even against agents with built-in safety mechanisms, requiring only the ability to post content on public websites. To address this risk, we propose comprehensive mitigation strategies including oversight mechanisms, execution constraints, and task-aware reasoning techniques.

cs.CR↗

arXiv2025

LexiMark: Robust Watermarking via Lexical Substitutions to Enhance Membership Verification of an LLM's Textual Training Data

Large language models (LLMs) can be trained or fine-tuned on data obtained without the owner's consent. Verifying whether a specific LLM was trained on particular data instances or an entire dataset is extremely challenging. Dataset watermarking addresses this by embedding identifiable modifications in training data to detect unauthorized use. However, existing methods often lack stealth, making them relatively easy to detect and remove. In light of these limitations, we propose LexiMark, a novel watermarking technique designed for text and documents, which embeds synonym substitutions for carefully selected high-entropy words. Our method aims to enhance an LLM's memorization capabilities on the watermarked text without altering the semantic integrity of the text. As a result, the watermark is difficult to detect, blending seamlessly into the text with no visible markers, and is resistant to removal due to its subtle, contextually appropriate substitutions that evade automated and manual detection. We evaluated our method using baseline datasets from recent studies and seven open-source models: LLaMA-1 7B, LLaMA-3 8B, Mistral 7B, Pythia 6.9B, as well as three smaller variants from the Pythia family (160M, 410M, and 1B). Our evaluation spans multiple training settings, including continued pretraining and fine-tuning scenarios. The results demonstrate significant improvements in AUROC scores compared to existing methods, underscoring our method's effectiveness in reliably verifying whether unauthorized watermarked data was used in LLM training.

cs.CL↗

arXiv2025

MIA-EPT: Membership Inference Attack via Error Prediction for Tabular Data

Synthetic data generation plays an important role in enabling data sharing, particularly in sensitive domains like healthcare and finance. Recent advances in diffusion models have made it possible to generate realistic, high-quality tabular data, but they may also memorize training records and leak sensitive information. Membership inference attacks (MIAs) exploit this vulnerability by determining whether a record was used in training. While MIAs have been studied in images and text, their use against tabular diffusion models remains underexplored despite the unique risks of structured attributes and limited record diversity. In this paper, we introduce MIAEPT, Membership Inference Attack via Error Prediction for Tabular Data, a novel black-box attack specifically designed to target tabular diffusion models. MIA-EPT constructs errorbased feature vectors by masking and reconstructing attributes of target records, disclosing membership signals based on how well these attributes are predicted. MIA-EPT operates without access to the internal components of the generative model, relying only on its synthetic data output, and was shown to generalize across multiple state-of-the-art diffusion models. We validate MIA-EPT on three diffusion-based synthesizers, achieving AUC-ROC scores of up to 0.599 and TPR@10% FPR values of 22.0% in our internal tests. Under the MIDST 2025 competition conditions, MIA-EPT achieved second place in the Black-box Multi-Table track (TPR@10% FPR = 20.0%). These results demonstrate that our method can uncover substantial membership leakage in synthetic tabular data, challenging the assumption that synthetic data is inherently privacy-preserving. Our code is publicly available at https://github.com/eyalgerman/MIA-EPT.

cs.CR↗

arXiv2025

Tag&Tab: Pretraining Data Detection in Large Language Models Using Keyword-Based Membership Inference Attack

Large language models (LLMs) have become essential tools for digital task assistance. Their training relies heavily on the collection of vast amounts of data, which may include copyright-protected or sensitive information. Recent studies on detecting pretraining data in LLMs have primarily focused on sentence- or paragraph-level membership inference attacks (MIAs), usually involving probability analysis of the target model's predicted tokens. However, these methods often exhibit poor accuracy, failing to account for the semantic importance of textual content and word significance. To address these shortcomings, we propose Tag&Tab, a novel approach for detecting data used in LLM pretraining. Our method leverages established natural language processing (NLP) techniques to tag keywords in the input text, a process we term Tagging. Then, the LLM is used to obtain probabilities for these keywords and calculate their average log-likelihood to determine input text membership, a process we refer to as Tabbing. Our experiments on four benchmark datasets (BookMIA, MIMIR, PatentMIA, and the Pile) and several open-source LLMs of varying sizes demonstrate an average increase in AUC scores ranging from 5.3% to 17.6% over state-of-the-art methods. Tag&Tab not only sets a new standard for data leakage detection in LLMs, but its outstanding performance is a testament to the importance of words in MIAs on LLMs.

cs.CR↗

arXiv2025

KubeGuard: LLM-Assisted Kubernetes Hardening via Configuration Files and Runtime Logs Analysis

The widespread adoption of Kubernetes (K8s) for orchestrating cloud-native applications has introduced significant security challenges, such as misconfigured resources and overly permissive configurations. Failing to address these issues can result in unauthorized access, privilege escalation, and lateral movement within clusters. Most existing K8s security solutions focus on detecting misconfigurations, typically through static analysis or anomaly detection. In contrast, this paper presents KubeGuard, a novel runtime log-driven recommender framework aimed at mitigating risks by addressing overly permissive configurations. KubeGuard is designed to harden K8s environments through two complementary tasks: Resource Creation and Resource Refinement. It leverages large language models (LLMs) to analyze manifests and runtime logs reflecting actual system behavior, using modular prompt-chaining workflows. This approach enables KubeGuard to create least-privilege configurations for new resources and refine existing manifests to reduce the attack surface. KubeGuard's output manifests are presented as recommendations that users (e.g., developers and operators) can review and adopt to enhance cluster security. Our evaluation demonstrates that KubeGuard effectively generates and refines K8s manifests for Roles, NetworkPolicies, and Deployments, leveraging both proprietary and open-source LLMs. The high precision, recall, and F1-scores affirm KubeGuard's practicality as a framework that translates runtime observability into actionable, least-privilege configuration guidance.

cs.CR↗

arXiv2025

Addressing Key Challenges of Adversarial Attacks and Defenses in the Tabular Domain: A Methodological Framework for Coherence and Consistency

Machine learning models trained on tabular data are vulnerable to adversarial attacks, even in realistic scenarios where attackers only have access to the model's outputs. Since tabular data contains complex interdependencies among features, it presents a unique challenge for adversarial samples which must maintain coherence and respect these interdependencies to remain indistinguishable from benign data. Moreover, existing attack evaluation metrics-such as the success rate, perturbation magnitude, and query count-fail to account for this challenge. To address those gaps, we propose a technique for perturbing dependent features while preserving sample coherence. In addition, we introduce Class-Specific Anomaly Detection (CSAD), an effective novel anomaly detection approach, along with concrete metrics for assessing the quality of tabular adversarial attacks. CSAD evaluates adversarial samples relative to their predicted class distribution, rather than a broad benign distribution. It ensures that subtle adversarial perturbations, which may appear coherent in other classes, are correctly identified as anomalies. We integrate SHAP explainability techniques to detect inconsistencies in model decision-making, extending CSAD for SHAP-based anomaly detection. Our evaluation incorporates both anomaly detection rates with SHAP-based assessments to provide a more comprehensive measure of adversarial sample quality. We evaluate various attack strategies, examining black-box query-based and transferability-based gradient attacks across four target models. Experiments on benchmark tabular datasets reveal key differences in the attacker's risk and effort and attack quality, offering insights into the strengths, limitations, and trade-offs faced by attackers and defenders. Our findings lay the groundwork for future research on adversarial attacks and defense development in the tabular domain.

cs.LG↗

arXiv2025

SoK: Cybersecurity Assessment of Humanoid Ecosystem

Humanoids are progressing toward practical deployment across healthcare, industrial, defense, and service sectors. While typically considered cyber-physical systems (CPSs), their dependence on traditional networked software stacks (e.g., Linux operating systems), robot operating system (ROS) middleware, and over-the-air update channels, creates a distinct security profile that exposes them to vulnerabilities conventional CPS models do not fully address. Prior studies have mainly examined specific threats, such as LiDAR spoofing or adversarial machine learning (AML). This narrow focus overlooks how an attack targeting one component can cascade harm throughout the robot's interconnected systems. We address this gap through a systematization of knowledge (SoK) that takes a comprehensive approach, consolidating fragmented research from robotics, CPS, and network security domains. We introduce a seven-layer security model for humanoid robots, organizing 39 known attacks and 35 defenses across the humanoid ecosystem-from hardware to human-robot interaction. Building on this security model, we develop a quantitative 39x35 attack-defense matrix with risk-weighted scoring, validated through Monte Carlo analysis. We demonstrate our method by evaluating three real-world robots: Pepper, G1 EDU, and Digit. The scoring analysis revealed varying security maturity levels, with scores ranging from 39.9% to 79.5% across the platforms. This work introduces a structured, evidence-based assessment method that enables systematic security evaluation, supports cross-platform benchmarking, and guides prioritization of security investments in humanoid robotics.

cs.CR↗

arXiv2025

GeNet: A Multimodal LLM-Based Co-Pilot for Network Topology and Configuration

Communication network engineering in enterprise environments is traditionally a complex, time-consuming, and error-prone manual process. Most research on network engineering automation has concentrated on configuration synthesis, often overlooking changes in the physical network topology. This paper introduces GeNet, a multimodal co-pilot for enterprise network engineers. GeNet is a novel framework that leverages a large language model (LLM) to streamline network design workflows. It uses visual and textual modalities to interpret and update network topologies and device configurations based on user intents. GeNet was evaluated on enterprise network scenarios adapted from Cisco certification exercises. Our results demonstrate GeNet's ability to interpret network topology images accurately, potentially reducing network engineers' efforts and accelerating network design processes in enterprise environments. Furthermore, we show the importance of precise topology understanding when handling intents that require modifications to the network's topology.

cs.NI↗

arXiv2025

FRAME : Comprehensive Risk Assessment Framework for Adversarial Machine Learning Threats

The widespread adoption of machine learning (ML) systems increased attention to their security and emergence of adversarial machine learning (AML) techniques that exploit fundamental vulnerabilities in ML systems, creating an urgent need for comprehensive risk assessment for ML-based systems. While traditional risk assessment frameworks evaluate conventional cybersecurity risks, they lack ability to address unique challenges posed by AML threats. Existing AML threat evaluation approaches focus primarily on technical attack robustness, overlooking crucial real-world factors like deployment environments, system dependencies, and attack feasibility. Attempts at comprehensive AML risk assessment have been limited to domain-specific solutions, preventing application across diverse systems. Addressing these limitations, we present FRAME, the first comprehensive and automated framework for assessing AML risks across diverse ML-based systems. FRAME includes a novel risk assessment method that quantifies AML risks by systematically evaluating three key dimensions: target system's deployment environment, characteristics of diverse AML techniques, and empirical insights from prior research. FRAME incorporates a feasibility scoring mechanism and LLM-based customization for system-specific assessments. Additionally, we developed a comprehensive structured dataset of AML attacks enabling context-aware risk assessment. From an engineering application perspective, FRAME delivers actionable results designed for direct use by system owners with only technical knowledge of their systems, without expertise in AML. We validated it across six diverse real-world applications. Our evaluation demonstrated exceptional accuracy and strong alignment with analysis by AML experts. FRAME enables organizations to prioritize AML risks, supporting secure AI deployment in real-world environments.

cs.LG↗