arXiv subjects

Elisa Bertino

Publications and source records attributed to Elisa Bertino.

At least 19 recordsLinked to original sources

arXiv2026

A2ABreak: Systematic Security Analysis of the A2A Protocol

The Agent2Agent (A2A) protocol, now governed by the Linux Foundation, is an open standard that enables autonomous AI agents to discover, authenticate with, and delegate tasks to one another across organizational boundaries. Designed to complement the Model Context Protocol (MCP) for tool integration, A2A is rapidly emerging as the horizontal communication layer of the multi-agent ecosystem. Yet the protocol's security has received no systematic analysis. This paper presents A2ABreak, the first rigorous systematic security analysis of the A2A protocol. We introduce a novel framework that utilizes an LLM-assisted extraction of a verified finite-state machine directly from the natural-language specification, producing a unified model of 37 states and 76 transitions from 929 formalized statements, and then systematically reasons over this model to discover protocol-level vulnerabilities through adversarial verification, under a full-compliance assumption. Our analysis uncovers 11 new vulnerabilities, each exploitable by a specification-compliant adversary without requiring any implementation flaw. Among the findings are cross-client context injection through unprotected context identifiers, credential harvesting via multi-hop identity loss in delegation chains, and data exfiltration through rogue agents advertising unattested capability claims. A2ABreak achieves 73.3% precision and 84.6% F1 against independent expert review, while a zero-shot LLM baseline operating over the same specification produces zero confirmed findings, demonstrating that explicit formal grounding is essential for sound protocol security analysis.

cs.CR↗

arXiv2026

A Lightweight Post-Quantum Authentication Framework for 5G Base Station Bootstrapping

The absence of authenticated bootstrapping between User Equipments (UEs) and Base Stations (BSs) in 5G leaves System Information Block (SIB) broadcasts unprotected, enabling fake BS attacks, man-in-the-middle interception, and spoofed emergency alerts. Prior efforts such as Public Key Infrastructure (PKI)-based certificate chains, token-based schemes, and identity-based signatures either impose overhead exceeding 5G's strict packet-size constraints or lack post-quantum (PQ) security. Direct NIST-PQC integration is also infeasible; current standards, such as ML-DSA requires 34 fragmented SIB1 packets and up to 5,282\,ms end-to-end delay, and FN-DSA still requires 13 fragments and up to 1,920\,ms. We propose EMULSION, a symmetric chained publicly verifiable authentication framework for 5G/6G BS broadcast authentication. EMULSION is the first framework to exploit native 5G architectural features: fixed SIB transmission windows, millisecond-level time synchronization, and eSIM/USIM credential management to achieve genuine PQ security at symmetric-key efficiency. It uses a timed stream loss-tolerant authentication with HMAC chain anchored by a compact PQ signature~(MAYO) applied once per epoch, fitting authentication within a single packet with no fragmentation and eliminating certificate transmission entirely. Unlike prior schemes, EMULSION extends to the full SIB family (SIB1-SIB21) at no additional per-broadcast cost, demonstrated over the air on SIB1. Evaluated on a real over-the-air 5G testbed, EMULSION achieves 33x lower end-to-end delay and 31x less communication overhead than ML-DSA, and 12x lower delay and 5.4x less overhead than FN-DSA. We formally prove the security of EMULSION and open-source its implementation for public testing and adaptation.

cs.CR↗

arXiv2026

PhantomCall: Evading ML Malware Detectors via Function Call Graph Perturbation

Prior adversarial attacks on Windows PE malware detectors target raw bytes, PE headers, or intra-function control-flow graphs, leaving the function call graph (FCG) unexplored as an attack surface. Yet the FCG structure is an important feature in graph-based malware detectors. We present Phan- tomCall, a black-box attack that perturbs the FCG of Windows PE malware by injecting fully executable dummy functions at targeted call sites, adding new nodes and edges to both the CFG and FCG while preserving program semantics. We pair this structural perturbation with classifier-guided search and tunable injection parameters, effective across three archi- tecturally distinct classifiers. Evaluated on a 2025-collected Windows malware corpus against MalConv (raw-byte CNN), MalGraph (graph-based GNN), and SAFE+GNN (pure FCG GNN trained from scratch on a 2024 corpus) at two FPR thresholds, the best PhantomCall variant achieves 85-100% attack success rate across all configurations, exceeding prior state-of-the-art by up to 14.78 percentage points on MalGraph and 95.5 percentage points on SAFE+GNN, and generating evasive variants up to 2.9x faster on average across all targets. For MalConv and MalGraph, the majority of evasions require only a single call site modification, and 86-97% of evaluated evasive variants preserve the original malicious behavior in sandbox-based semantic testing across all configurations.

cs.CR↗

arXiv2026

Abstractions for Network Intelligence: A Reference Architecture for AI at the Wireless Edge

Networks are increasingly adopting AI as are AI applications leveraging networks. Awareness sharing between networks and AI applications promises to unlock higher levels of network utilization and application performance, but is inadequately supported in the current architecture of the Internet. In this paper, we describe a reference architecture that abstractly enables the synergistic interaction of intelligent applications and the intelligent network, via an information waist, and also supports the network intelligence services in the emerging intelligence plane in networks. We discuss the rationale for our AI-EDGE architecture, its functional requirements, and the core abstractions. We present a reference component-level design of the core abstractions to support experimentation and development on existing platforms for wireless networking (i.e., based on O-RAN cellular networks) and edge computing (i.e., based on 3GPP Edge App and ETSI MEC). Moreover, we provide representative use cases from the perspective of different types of users that demonstrate the benefits of the architecture in contexts of awareness sharing, portability, prototyping, and validation.

cs.NI↗

arXiv2026

ChronoMem: Version Control and Semantic Rollback for Large Language Model Agent Memory

LLM agents increasingly rely on long-term memory to support multi-session interaction and personalization. However, existing agent memory systems are designed around forward-only evolution, continuously accumulating, consolidating, and overwriting knowledge, with no principled mechanism to inspect, version, or revert prior states. This makes agents brittle under corrections, concept drift, and memory corruption, particularly after they have already been exposed to subsequent information. We present ChronoMem, a semantic version-control layer for agentic memory integrated into the production-ready, open-source Agent Development Kit by Google. ChronoMem commits whole-memory snapshots at each memory write, maintains structured version histories, and supports natural-language rollback requests by mapping undo intents to concrete historical versions through hybrid lexical and semantic retrieval, rank fusion, and reranking. We further introduce a post-exposure evaluation protocol that tests whether an agent can behave counterfactually after rollback by answering queries and summarizing history as if future updates had never occurred. On long-horizon conversational benchmarks augmented with evolving memory states and rollback tasks, ChronoMem substantially improves rollback-consistent question answering and history summarization relative to prompt-only and retrieval-only baselines, while achieving strong performance in semantic version selection. To our knowledge, ChronoMem is the first open-source system and benchmark for systematic semantic global memory rollback in LLM agents.

cs.CL↗

arXiv2026

Securing Agentic AI: From Per-Action Checks to Trajectory Assurance

Autonomous agents are increasingly used to execute consequential tasks in environments governed by operational constraints, organizational policies, regulatory requirements, and technical standards. Their safety is therefore determined not by the correctness of individual actions, but by whether their overall behavior remains consistent with the rules and invariants of the systems in which they operate. As large language model (LLM)-based agents become more autonomous and increasingly delegate tasks across organizational boundaries, securing them evolves from a single challenge into a broad and interconnected landscape spanning the entire agentic stack. At the single-agent level, untrusted inputs through prompts, memory, retrieved knowledge, and tool interfaces create attack surfaces. In multi-agent settings, delegation and communication introduce challenges related to identity, trust, capability control, and decision transparency, while the underlying model routing and execution control plane remains vulnerable to manipulation and to unverified model provenance. Perhaps the most fundamental challenge is behavioral containment: sequences of individually permissible actions may collectively violate system-level constraints and safety invariants. At the broader level, supply-chain integrity, provenance, accountability, and end-to-end observability remain largely open problems. A common principle unifies these directions: security must become a verifiable property of the architectures, protocols, and runtimes that govern agent behavior, rather than an optional layer of guidance. Charting these challenges provides a roadmap toward trustworthy autonomous agent deployment.

cs.AI↗

arXiv2026

Zero-Trust Strategies for O-RAN Cellular Networks: Principles, Challenges and Research Directions

Cellular networks are foundational to modern communication, supporting a broad range of applications, from civilian use to enterprise systems and military tactical networks. The advent of fifth-generation and beyond cellular networks (B5G) introduces emerging compute capabilities into the Radio Access Network (RAN), transforming it from a traditionally closed, vendor-locked infrastructure into an open and programmable ecosystem. This evolution, exemplified by Open-RAN (O-RAN), enables the deployment of control-plane applications from diverse sources, which can dynamically influence user-plane traffic in response to real-time events. As cellular infrastructures become more disaggregated and software-driven, security becomes an increasingly critical concern. Zero-Trust Architecture (ZTA) has emerged as a promising security paradigm that discards implicit trust assumptions by acknowledging that threats may arise from both external and internal sources. ZTA mandates comprehensive and fine-grained security mechanisms across both control and user planes to contain adversarial movements and enhance breach detection and attack response actions. In this paper, we explore the adoption of ZTA in the context of 5G and beyond, with a particular focus on O-RAN as an architectural enabler. We analyze how ZTA principles align with the architectural and operational characteristics of O-RAN, and identify key challenges and opportunities for embedding zero-trust mechanisms within O-RAN-based cellular networks.

cs.CR↗

arXiv2026

Future-Proofing Authentication Against Insecure Bootstrapping for 5G Networks: Feasibility, Resiliency, and Accountability

The 5G protocol lacks a robust base station (BS) authentication mechanism during the initial bootstrapping phase, leaving it susceptible to fake BSs, spoofed broadcasts, and large-scale manipulation of System Information Blocks (SIBs). Existing solutions incur high communication overhead, rely on centralized trust, and lack accountability and long-term breach resiliency. Given the inevitability of BS compromise and the severe impact of forged SIBs as the root of trust (e.g., fake alerts, tracking, false roaming), distributed trust, verifiable forgery detection, and audit logging are essential yet remain largely unexplored. These challenges are further amplified by the emergence of quantum-capable adversaries. While NIST Post-Quantum Cryptography (PQC) standards are widely viewed as a path toward long-term security, their feasibility under 5G's strict packet-size, latency, and broadcast constraints has not been systematically studied. This work presents, to our knowledge, the first comprehensive network-level performance characterization of integrating NIST-PQC standards and conventional digital signatures into 5G BS authentication, showing that direct PQC adoption is impractical due to excessive signature sizes, fragmentation, and protocol-level delays. To address these challenges, we propose BORG, a future-proof authentication framework based on a Hierarchical Identity-Based Threshold Signature with Fail-Stop (HITFS) properties. BORG distributes trust across multiple BSs via threshold signing, enables post-mortem verifiable forgery detection, and provides tamper-evident, PQ-secure audit logging, while maintaining compact signatures that fit within a single SIB1 packet without fragmentation and incurring minimal UE overhead, as validated through our real over-the-air 5G testbed implementation.

cs.CR↗

arXiv2026

Information Theoretic Adversarial Training of Large Language Models

Large language models (LLMs) remain vulnerable to adversarial prompting despite advances in alignment and safety, often exhibiting harmful behaviors under novel attack strategies. While adversarial training can improve robustness, existing approaches are computationally expensive and difficult to scale. Recent continuous adversarial training methods, such as Continuous adversarial training (CAT) and Continuous Adversarial Preference Optimization (CAPO), address this challenge by leveraging gradient-based perturbations in the embedding space, enabling more efficient and expressive attacks. Building on this paradigm, we propose WARDEN, a distributionally robust adversarial training framework for LLMs that dynamically reweights adversarial examples through an f -divergence ambiguity set around the empirical training distribution. Our method optimizes the worst-case adversarial loss within a divergence ball around the empirical data distribution, automatically emphasizing harder adversarial examples. Using the convex dual formulation, the objective reduces to a log-sum-exp form under the KL divergence, with a dynamical parameter controlling the strength of reweighting. This study leads to a new class of information-theoretic objectives that significantly reduce attack success rates while maintaining model utility. Across multiple LLMs and attack settings, WARDEN substantially reduces attack success rates with computational and utility costs comparable to CAT-, CAPO-, and MixAT-based baselines, making it a practical approach for scalable robust alignment.

cs.LG↗

arXiv2026

Quantum-Resistant Networks: A Review of Primitives, Protocols and Best Practices

Large-scale quantum computers threaten the public-key cryptographic foundations underpinning today's network security infrastructures. While significant progress has been made in standardizing post-quantum cryptographic (PQC) primitives and adapting individual protocols such as TLS and SSH, far less attention has been paid to the broader architectural consequences of the post-quantum transition for networked systems. In particular, many real-world deployments such as mobile networks, industrial control systems, IoT environments, and regulated infrastructures cannot assume the universal availability, deployability, or desirability of PQ public-key infrastructures. This paper presents the first comprehensive systematization of PQ-resistant network architectures, focusing on key distribution and management as a system-level design problem rather than a protocol-local substitution. We introduce a unified taxonomy spanning cryptographic foundations (symmetric-only, PQ-PKI, hybrid, and information-theoretic multi-path), key-distribution architectures (centralized, hierarchical, replicated, threshold, MPC-backed, and serverless), trust and threat models, key-management lifecycle, and deployment environments. Using this framework, we analyze the security, scalability, and operational trade-offs of a wide range of architectures under realistic PQ adversary assumptions, including harvest-now, decrypt-later attacks and partial infrastructure compromise. Our study highlights fundamental gaps in existing approaches, clarifies when PQ-PKI is necessary or avoidable, and identifies promising research directions for building cryptographically agile, quantum-resilient network infrastructures.

cs.CR↗

arXiv2026

Can Drift-Adaptive Malware Detectors Be Made Robust? Attacks and Defenses Under White-Box and Black-Box Threats

Concept drift and adversarial evasion are two major challenges for deploying machine learning-based malware detectors. While both have been studied separately, their combination, the adversarial robustness of drift-adaptive detectors, remains unexplored. We address this problem with AdvDA, a recent malware detector that uses adversarial domain adaptation to align a labeled source domain with a target domain with limited labels. The distribution shift between domains poses a unique challenge: robustness learned on the source may not transfer to the target, and existing defenses assume a fixed distribution. To address this, we propose a universal robustification framework that fine-tunes a pretrained AdvDA model on adversarially transformed inputs, agnostic to the attack type and choice of transformations. We instantiate it with five defense variants spanning two threat models: white-box PGD attacks in the feature space and black-box MalGuise attacks that modify malware binaries via functionality-preserving control-flow mutations. Across nine defense configurations, five monthly adaptation windows on Windows malware, and three false-positive-rate operating points, we find the undefended AdvDA completely vulnerable to PGD (100% attack success) and moderately to MalGuise (13%). Our framework reduces these rates to as low as 3.2% and 5.1%, respectively, but the optimal strategy differs: source adversarial training is essential for PGD defenses yet counterproductive for MalGuise defenses, where target-only training suffices. Furthermore, robustness does not transfer across these two threat models. We provide deployment recommendations that balance robustness, detection accuracy, and computational cost.

cs.CR↗

arXiv2026

Breaking 5G on The Lower Layer

As 3GPP systems have strengthened security at the upper layers of the cellular stack, plaintext PHY and MAC layers have remained relatively understudied, though interest in them is growing. In this work, we explore lower-layer exploitation in modern 5G, where recent releases have increased the number of lower-layer control messages and procedures, creating new opportunities for practical attacks. We present two practical attacks and evaluate them in a controlled lab testbed. First, we reproduce a SIB1 spoofing attack to study manipulations of unprotected broadcast fields. By repeatedly changing a key parameter, the UE is forced to refresh and reacquire system information, keeping the radio interface active longer than necessary and increasing battery consumption. Second, we demonstrate a new Timing Advance (TA) manipulation attack during the random access procedure. By injecting an attacker-chosen TA offset in the random access response, the victim applies incorrect uplink timing, which leads to uplink desynchronization, radio link failures, and repeated reconnection loops that effectively cause denial of service. Our experiments use commercial smartphones and open-source 5G network software. Experimental results in our testbed demonstrate that TA offsets exceeding a small tolerance reliably trigger radio link failures in our testbed and can keep devices stuck in repeated re-establishment attempts as long as the rogue base station remains present. Overall, our findings highlight that compact lower-layer control messages can have a significant impact on availability and power, and they motivate placing defenses for initial access and broadcast procedures.

cs.CR↗

arXiv2026

A Survey of Agentic AI and Cybersecurity: Challenges, Opportunities and Use-case Prototypes

Agentic AI marks an important transition from single-step generative models to systems capable of reasoning, planning, acting, and adapting over long-lasting tasks. By integrating memory, tool use, and iterative decision cycles, these systems enable continuous, autonomous workflows in real-world environments. This survey examines the implications of agentic AI for cybersecurity. On the defensive side, agentic capabilities enable continuous monitoring, autonomous incident response, adaptive threat hunting, and fraud detection at scale. Conversely, the same properties amplify adversarial power by accelerating reconnaissance, exploitation, coordination, and social-engineering attacks. These dual-use dynamics expose fundamental gaps in existing governance, assurance, and accountability mechanisms, which were largely designed for non-autonomous and short-lived AI systems. To address these challenges, we survey emerging threat models, security frameworks, and evaluation pipelines tailored to agentic systems, and analyze systemic risks including agent collusion, cascading failures, oversight evasion, and memory poisoning. Finally, we present three representative use-case implementations that illustrate how agentic AI behaves in practical cybersecurity workflows, and how design choices shape reliability, safety, and operational effectiveness.

cs.CR↗

arXiv2025

CAT: Can Trust be Predicted with Context-Awareness in Dynamic Heterogeneous Networks?

Trust prediction provides valuable support for decision-making, risk mitigation, and system security enhancement. Recently, Graph Neural Networks (GNNs) have emerged as a promising approach for trust prediction, owing to their ability to learn expressive node representations that capture intricate trust relationships within a network. However, current GNN-based trust prediction models face several limitations: (i) Most of them fail to capture trust dynamicity, leading to questionable inferences. (ii) They rarely consider the heterogeneous nature of real-world networks, resulting in a loss of rich semantics. (iii) None of them support context-awareness, a basic property of trust, making prediction results coarse-grained. To this end, we propose CAT, the first Context-Aware GNN-based Trust prediction model that supports trust dynamicity and accurately represents real-world heterogeneity. CAT consists of a graph construction layer, an embedding layer, a heterogeneous attention layer, and a prediction layer. It handles dynamic graphs using continuous-time representations and captures temporal information through a time encoding function. To model graph heterogeneity and leverage semantic information, CAT employs a dual attention mechanism that identifies the importance of different node types and nodes within each type. For context-awareness, we introduce a new notion of meta-paths to extract contextual features. By constructing context embeddings and integrating a context-aware aggregator, CAT can predict both context-aware trust and overall trust. Extensive experiments on three real-world datasets demonstrate that CAT outperforms five groups of baselines in trust prediction, while exhibiting strong scalability to large-scale graphs and robustness against both trust-oriented and GNN-oriented attacks.

cs.LG↗

arXiv2025

Proving DNSSEC Correctness: A Formal Approach to Secure Domain Name Resolution

The Domain Name System Security Extensions (DNSSEC) are critical for preventing DNS spoofing, yet its specifications contain ambiguities and vulnerabilities that elude traditional "break-and-fix" approaches. A holistic, foundational security analysis of the protocol has thus remained an open problem. This paper introduces DNSSECVerif, the first framework for comprehensive, automated formal security analysis of the DNSSEC protocol suite. Built on the SAPIC+ symbolic verifier, our high-fidelity model captures protocol-level interactions, including cryptographic operations and stateful caching with fine-grained concurrency control. Using DNSSECVerif, we formally prove four of DNSSEC's core security guarantees and uncover critical ambiguities in the standards--notably, the insecure coexistence of NSEC and NSEC3. Our model also automatically rediscovers three classes of known attacks, demonstrating fundamental weaknesses in the protocol design. To bridge the model-to-reality gap, we validate our findings through targeted testing of mainstream DNS software and a large-scale measurement study of over 2.2 million open resolvers, confirming the real-world impact of these flaws. Our work provides crucial, evidence-based recommendations for hardening DNSSEC specifications and implementations.

cs.CR↗

arXiv2025

LFreeDA: Label-Free Drift Adaptation for Windows Malware Detection

Machine learning (ML)-based malware detectors degrade over time as concept drift introduces new and evolving families unseen during training. Retraining is limited by the cost and time of manual labeling or sandbox analysis. Existing approaches mitigate this via drift detection and selective labeling, but fully label-free adaptation remains largely unexplored. Recent self-training methods use a previously trained model to generate pseudo-labels for unlabeled data and then train a new model on these labels. The unlabeled data are used only for inference and do not participate in training the earlier model. We argue that these unlabeled samples still carry valuable information that can be leveraged when incorporated appropriately into training. This paper introduces LFreeDA, an end-to-end framework that adapts malware classifiers to drift without manual labeling or drift detection. LFreeDA first performs unsupervised domain adaptation on malware images, jointly training on labeled and unlabeled samples to infer pseudo-labels and prune noisy ones. It then adapts a classifier on CFG representations using the labeled and selected pseudo-labeled data, leveraging the scalability of images for pseudo-labeling and the richer semantics of CFGs for final adaptation. Evaluations on the real-world MB-24+ dataset show that LFreeDA improves accuracy by up to 12.6% and F1 by 11.1% over no-adaptation lower bounds, and is only 4% and 3.4% below fully supervised upper bounds in accuracy and F1, respectively. It also matches the performance of state-of-the-art methods provided with ground truth labels for 300 target samples. Additional results on two controlled-drift benchmarks further confirm that LFreeDA maintains malware detection performance as malware evolves without human labeling.

cs.CR↗

arXiv2025

Automated Vulnerability Validation and Verification: A Large Language Model Approach

Software vulnerabilities remain a critical security challenge, providing entry points for attackers into enterprise networks. Despite advances in security practices, the lack of high-quality datasets capturing diverse exploit behavior limits effective vulnerability assessment and mitigation. This paper introduces an end-to-end multi-step pipeline leveraging generative AI, specifically large language models (LLMs), to address the challenges of orchestrating and reproducing attacks to known software vulnerabilities. Our approach extracts information from CVE disclosures in the National Vulnerability Database, augments it with external public knowledge (e.g., threat advisories, code snippets) using Retrieval-Augmented Generation (RAG), and automates the creation of containerized environments and exploit code for each vulnerability. The pipeline iteratively refines generated artifacts, validates attack success with test cases, and supports complex multi-container setups. Our methodology overcomes key obstacles, including noisy and incomplete vulnerability descriptions, by integrating LLMs and RAG to fill information gaps. We demonstrate the effectiveness of our pipeline across different vulnerability types, such as memory overflows, denial of service, and remote code execution, spanning diverse programming languages, libraries and years. In doing so, we uncover significant inconsistencies in CVE descriptions, emphasizing the need for more rigorous verification in the CVE disclosure process. Our approach is model-agnostic, working across multiple LLMs, and we open-source the artifacts to enable reproducibility and accelerate security research. To the best of our knowledge, this is the first system to systematically orchestrate and exploit known vulnerabilities in containerized environments by combining general-purpose LLM reasoning with CVE data and RAG-based context enrichment.

cs.CR↗

arXiv2025

TIMESAFE: Timing Interruption Monitoring and Security Assessment for Fronthaul Environments

5G and beyond cellular systems embrace the disaggregation of Radio Access Network (RAN) components, exemplified by the evolution of the fronthaul (FH) connection between cellular baseband and radio unit equipment. Crucially, synchronization over the FH is pivotal for reliable 5G services. In recent years, there has been a push to move these links to an Ethernet-based packet network topology, leveraging existing standards and ongoing research for Time-Sensitive Networking (TSN). However, TSN standards, such as Precision Time Protocol (PTP), focus on performance with little to no concern for security. This increases the exposure of the open FH to security risks. Attacks targeting synchronization mechanisms pose significant threats, potentially disrupting 5G networks and impairing connectivity. In this paper, we demonstrate the impact of successful spoofing and replay attacks against PTP synchronization. We show how a spoofing attack is able to cause a production-ready O-RAN and 5G-compliant private cellular base station to catastrophically fail within 2 seconds of the attack, necessitating manual intervention to restore full network operations. To counter this, we design a Machine Learning (ML)-based monitoring solution capable of detecting various malicious attacks with over 97.5% accuracy.

cs.NI↗