arXiv subjects

Md Raz

Publications and source records attributed to Md Raz.

5 recordsLinked to original sources

arXiv2026

Sandbox-Enabled Digital Twin for Cyber-Physical Systems

Firmware/software in cyber-physical system (CPS) embedded devices/controllers can have vulnerabilities stemming from multiple sources such as weak security practices, outdated libraries, or supply chain attacks that induce adversarial effects under plant state-based triggers. However, pre-deployment validation of CPS controllers typically relies on digital twins that model controller logic as a black box. On the other hand, side channel monitoring and anomaly detection of CPS controller firmware/software is complementary, but is typically exercised with synthetic inputs or under specific CPS operational profiles and does not simultaneously track software execution and CPS plant evolution. To bridge this gap, we present a closed-loop digital twin framework that hosts unmodified controller binaries in a Linux sandbox (SaMOSA) with its I/O rerouted to an external plant simulator. The framework captures four time-synchronized side channels (hardware performance counters, system calls, disk activity, network activity) alongside plant state and provides orchestration hooks for automated, repeatable, parameterized runs. We demonstrate the framework on an OpenPLC runtime controlling a Modbus-connected IEEE 14-bus power system, and also briefly discuss application to robotics systems. The synchronized traces correlate internal controller execution with plant events, providing an observability foundation for online testing, coverage analysis, and vulnerability detection.

eess.SY↗

arXiv2026

Safeguarding LLMs Against Misuse and AI-Driven Malware Using Steganographic Canaries

AI-powered malware increasingly exploits cloud-hosted generative-AI services and large language models (LLMs) as analysis engines for reconnaissance and code generation. Simultaneously, enterprise uploads expose sensitive documents to third-party AI vendors. Both threats converge at the AI service ingestion boundary, yet existing defenses focus on endpoints and network perimeters, leaving organizations with limited visibility once plaintext reaches an LLM service. To address this, we present a framework based on steganographic canary files: realistic documents carrying cryptographically derived identifiers embedded via complementary encoding channels. A pre-ingestion filter extracts and verifies these identifiers before LLM processing, enabling passive, format-agnostic detection without semantic classification. We support two modes of operation where Mode A marks existing sensitive documents with layered symbolic encodings (whitespace substitution, zero-width character insertion, homoglyph substitution), while Mode B generates synthetic canary documents using linguistic steganography (arithmetic coding over GPT-2), augmented with compatible symbolic layers. We model increasing document pre-processing and adversarial capability for both modes via a four-tier transport-transform taxonomy: All methods achieve 100% identifier recovery under benign and sanitization workflows (Tiers 1-2). The hybrid Mode B maintains 97% through targeted adversarial transforms (Tier 3). An end-to-end case study against an LLM-orchestrated ransomware pipeline confirms that both modes detect and block canary-bearing uploads before file encryption begins. To our knowledge, this is the first framework to systematically combine symbolic and linguistic text steganography into layered canary documents for detecting unauthorized LLM processing, evaluated against a transport-threat taxonomy tailored to AI malware.

cs.CR↗

arXiv2025

Ransomware 3.0: Self-Composing and LLM-Orchestrated

Using automated reasoning, code synthesis, and contextual decision-making, we introduce a new threat that exploits large language models (LLMs) to autonomously plan, adapt, and execute the ransomware attack lifecycle. Ransomware 3.0 represents the first threat model and research prototype of LLM-orchestrated ransomware. Unlike conventional malware, the prototype only requires natural language prompts embedded in the binary; malicious code is synthesized dynamically by the LLM at runtime, yielding polymorphic variants that adapt to the execution environment. The system performs reconnaissance, payload generation, and personalized extortion, in a closed-loop attack campaign without human involvement. We evaluate this threat across personal, enterprise, and embedded environments using a phase-centric methodology that measures quantitative fidelity and qualitative coherence in each attack phase. We show that open source LLMs can generate functional ransomware components and sustain closed-loop execution across diverse environments. Finally, we present behavioral signals and multi-level telemetry of Ransomware 3.0 through a case study to motivate future development of better defenses and policy enforcements to address novel AI-enabled ransomware attacks.

cs.CR↗

arXiv2025

SHIELD: A Host-Independent Framework for Ransomware Detection using Deep Filesystem Features

Ransomware's escalating sophistication necessitates tamper-resistant, off-host detection solutions that capture deep disk activity beyond the reach of a compromised operating system. Existing detection systems use host/kernel signals or rely on coarse block-I/O statistics, which are easy to evade and miss filesystem semantics. The filesystem layer itself remains underexplored as a source of robust indicators for storage-controller-level defense. To address this, we present SHIELD: a Secure Host-Independent Extensible Metric Logging Framework for Tamper-Proof Detection and Real-Time Mitigation of Ransomware Threats. SHIELD parses and logs filesystem-level features that cannot be evaded or obfuscated to expose deep disk activity for real-time ML-based detection and mitigation. We evaluate the efficacy of these metrics through experiments with both binary (benign vs. malicious behavior) and multiclass (ransomware strain identification) classifiers. In evaluations across diverse ransomware families, the best binary classifier achieves 97.29% accuracy in identifying malicious disk behavior. A hardware-only feature set that excludes all transport-layer metrics retains 95.97% accuracy, confirming feasibility for FPGA/ASIC deployment within the storage controller datapath. In a proof-of-concept closed-loop deployment, SHIELD halts disk operations within tens of disk actions, limiting targeted files affected to <0.4% for zero-shot strains at small action-windows, while maintaining low false-positive rates (<3.6%) on unseen benign applications. Results demonstrate that filesystem-aware, off-host telemetry enables accurate, resilient ransomware detection, including intermittent/partial encryption, and is practical for embedded integration in storage controllers or alongside other defense mechanisms.

cs.CR↗

arXiv2024

OffRAMPS: An FPGA-based Intermediary for Analysis and Modification of Additive Manufacturing Control Systems

Cybersecurity threats in Additive Manufacturing (AM) are an increasing concern as AM adoption continues to grow. AM is now being used for parts in the aerospace, transportation, and medical domains. Threat vectors which allow for part compromise are particularly concerning, as any failure in these domains would have life-threatening consequences. A major challenge to investigation of AM part-compromises comes from the difficulty in evaluating and benchmarking both identified threat vectors as well as methods for detecting adversarial actions. In this work, we introduce a generalized platform for systematic analysis of attacks against and defenses for 3D printers. Our "OFFRAMPS" platform is based on the open-source 3D printer control board "RAMPS." OFFRAMPS allows analysis, recording, and modification of all control signals and I/O for a 3D printer. We show the efficacy of OFFRAMPS by presenting a series of case studies based on several Trojans, including ones identified in the literature, and show that OFFRAMPS can both emulate and detect these attacks, i.e., it can both change and detect arbitrary changes to the g-code print commands.

cs.CR↗