arXiv subjects

Prashanth Krishnamurthy

Publications and source records attributed to Prashanth Krishnamurthy.

At least 37 records · Page 2Linked to original sources

arXiv2026

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↗

arXiv2025

OSVI-WM: One-Shot Visual Imitation for Unseen Tasks using World-Model-Guided Trajectory Generation

Visual imitation learning enables robotic agents to acquire skills by observing expert demonstration videos. In the one-shot setting, the agent generates a policy after observing a single expert demonstration without additional fine-tuning. Existing approaches typically train and evaluate on the same set of tasks, varying only object configurations, and struggle to generalize to unseen tasks with different semantic or structural requirements. While some recent methods attempt to address this, they exhibit low success rates on hard test tasks that, despite being visually similar to some training tasks, differ in context and require distinct responses. Additionally, most existing methods lack an explicit model of environment dynamics, limiting their ability to reason about future states. To address these limitations, we propose a novel framework for one-shot visual imitation learning via world-model-guided trajectory generation. Given an expert demonstration video and the agent's initial observation, our method leverages a learned world model to predict a sequence of latent states and actions. This latent trajectory is then decoded into physical waypoints that guide the agent's execution. Our method is evaluated on two simulated benchmarks and three real-world robotic platforms, where it consistently outperforms prior approaches, with over 30% improvement in some cases. The code is available at https://github.com/raktimgg/osvi-wm.

cs.RO↗

arXiv2025

Safe Multi-Robotic Arm Interaction via 3D Convex Shapes

Inter-robot collisions pose a significant safety risk when multiple robotic arms operate in close proximity. We present an online collision avoidance methodology leveraging High-Order Control Barrier Functions (HOCBFs) constructed for safe interactions among 3D convex shapes to address this issue. While prior works focused on using Control Barrier Functions (CBFs) for human-robotic arm and single-arm collision avoidance, we explore the problem of collision avoidance between multiple robotic arms operating in a shared space. In our methodology, we utilize the proposed HOCBFs as centralized and decentralized safety filters. These safety filters are compatible with many nominal controllers and ensure safety without significantly restricting the robots' workspace. A key challenge in implementing these filters is the computational overhead caused by the large number of safety constraints and the computation of a Hessian matrix per constraint. We address this challenge by employing numerical differentiation methods to approximate computationally intensive terms. The effectiveness of our method is demonstrated through extensive simulation studies and real-world experiments with Franka Research 3 robotic arms. The project video is available at this link.

cs.RO↗

arXiv2025

Grounding LLMs For Robot Task Planning Using Closed-loop State Feedback

Planning algorithms decompose complex problems into intermediate steps that can be sequentially executed by robots to complete tasks. Recent works have employed Large Language Models (LLMs) for task planning, using natural language to generate robot policies in both simulation and real-world environments. LLMs like GPT-4 have shown promising results in generalizing to unseen tasks, but their applicability is limited due to hallucinations caused by insufficient grounding in the robot environment. The robustness of LLMs in task planning can be enhanced with environmental state information and feedback. In this paper, we introduce a novel approach to task planning that utilizes two separate LLMs for high-level planning and low-level control, improving task-related success rates and goal condition recall. Our algorithm, \textit{BrainBody-LLM}, draws inspiration from the human neural system, emulating its brain-body architecture by dividing planning across two LLMs in a structured, hierarchical manner. BrainBody-LLM implements a closed-loop feedback mechanism, enabling learning from simulator errors to resolve execution errors in complex settings. We demonstrate the successful application of BrainBody-LLM in the VirtualHome simulation environment, achieving a 29\% improvement in task-oriented success rates over competitive baselines with the GPT-4 backend. Additionally, we evaluate our algorithm on seven complex tasks using a realistic physics simulator and the Franka Research 3 robotic arm, comparing it with various state-of-the-art LLMs. Our results show advancements in the reasoning capabilities of recent LLMs, which enable them to learn from raw simulator/controller errors to correct plans, making them highly effective in robotic task planning.

cs.RO↗

arXiv2025

Binary Diff Summarization using Large Language Models

Security of software supply chains is necessary to ensure that software updates do not contain maliciously injected code or introduce vulnerabilities that may compromise the integrity of critical infrastructure. Verifying the integrity of software updates involves binary differential analysis (binary diffing) to highlight the changes between two binary versions by incorporating binary analysis and reverse engineering. Large language models (LLMs) have been applied to binary analysis to augment traditional tools by producing natural language summaries that cybersecurity experts can grasp for further analysis. Combining LLM-based binary code summarization with binary diffing can improve the LLM's focus on critical changes and enable complex tasks such as automated malware detection. To address this, we propose a novel framework for binary diff summarization using LLMs. We introduce a novel functional sensitivity score (FSS) that helps with automated triage of sensitive binary functions for downstream detection tasks. We create a software supply chain security benchmark by injecting 3 different malware into 6 open-source projects which generates 104 binary versions, 392 binary diffs, and 46,023 functions. On this, our framework achieves a precision of 0.98 and recall of 0.64 for malware detection, displaying high accuracy with low false positives. Across malicious and benign functions, we achieve FSS separation of 3.0 points, confirming that FSS categorization can classify sensitive functions. We conduct a case study on the real-world XZ utils supply chain attack; our framework correctly detects the injected backdoor functions with high FSS.

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

SCAMPER -- Synchrophasor Covert chAnnel for Malicious and Protective ERrands

We note that constituent fields (notably the fraction-of-seconds timestamp field) in the data payload structure of the synchrophasor communication protocol (IEEE C37.118 standard) are overprovisioned relative to real-world usage and needs, lending themselves to abuse for embedding of covert channels. We develop the SCAMPER (Synchrophasor Covert Channel for Malicious and Protective ERrands) framework to exploit these overprovisioned fields for covert communication and show that SCAMPER can be applied for both malicious (attack) and protective (defense) purposes. Through modifications of the timestamp field, we demonstrate that SCAMPER enables an attacker to accomplish surreptitious communications between devices in the power system to trigger a variety of malicious actions. These timestamp modifications can be performed without having any impact on the operation of the power system. However, having recognized the potential for this covert channel, we show that SCAMPER can instead be applied for defensive security purposes as an integrated cryptographic data integrity mechanism that can facilitate detection of false data injection (FDI) attacks. We perform experimental studies of the proposed methods on two Hardware-in-the-Loop (HIL) testbeds to demonstrate the effectiveness of the proposed SCAMPER framework for both malicious and protective purposes.

cs.CR↗

arXiv2025

Enabling Deep Visibility into VxWorks-Based Embedded Controllers in Cyber-Physical Systems for Anomaly Detection

We propose the DIVER (Defensive Implant for Visibility into Embedded Run-times) framework for real-time deep visibility into embedded control devices in cyber-physical systems (CPSs). DIVER enables run-time detection of anomalies and targets devices running VxWorks real-time operating system (RTOS), precluding traditional methods of implementing dynamic monitors using OS (e.g., Linux, Windows) functions. DIVER has two components: "measurer" implant embedded into VxWorks kernel to collect run-time measurements and provide interactive/streaming interfaces over TCP/IP; remote "listener" that acquires and analyzes measurements and provides interactive user interface. DIVER focuses on small embedded devices with stringent resource constraints (e.g., insufficient storage to locally store measurements). To show efficacy and scalability of DIVER, we demonstrate on two embedded devices with different processor architectures and VxWorks versions: Motorola ACE Remote Terminal Unit used in CPS including power systems and Raspberry Pi representative of Internet-of-Things (IoT) applications.

cs.CR↗

arXiv2025

MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping

Robotic manipulation of unseen objects via natural language commands remains challenging. Language driven robotic grasping (LDRG) predicts stable grasp poses from natural language queries and RGB-D images. We propose MapleGrasp, a novel framework that leverages mask-guided feature pooling for efficient vision-language driven grasping. Our two-stage training first predicts segmentation masks from CLIP-based vision-language features. The second stage pools features within these masks to generate pixel-level grasp predictions, improving efficiency, and reducing computation. Incorporating mask pooling results in a 7% improvement over prior approaches on the OCID-VLG benchmark. Furthermore, we introduce RefGraspNet, an open-source dataset eight times larger than existing alternatives, significantly enhancing model generalization for open-vocabulary grasping. MapleGrasp scores a strong grasping accuracy of 89\% when compared with competing methods in the RefGraspNet benchmark. Our method achieves comparable performance to larger Vision-Language-Action models on the LIBERO benchmark, and shows significantly better generalization to unseen tasks. Real-world experiments on a Franka arm demonstrate 73% success rate with unseen objects, surpassing competitive baselines by 11%. Code is provided in our github repository.

cs.RO↗

arXiv2025

Collision Avoidance for Convex Primitives via Differentiable Optimization Based High-Order Control Barrier Functions

Ensuring the safety of dynamical systems is crucial, where collision avoidance is a primary concern. Recently, control barrier functions (CBFs) have emerged as an effective method to integrate safety constraints into control synthesis through optimization techniques. However, challenges persist when dealing with convex primitives and tasks requiring torque control, as well as the occurrence of unintended equilibria. This work addresses these challenges by introducing a high-order CBF (HOCBF) framework for collision avoidance among convex primitives. We transform nonconvex safety constraints into linear constraints by differentiable optimization and prove the high-order continuous differentiability. Then, we employ HOCBFs to accommodate torque control, enabling tasks involving forces or high dynamics. Additionally, we analyze the issue of spurious equilibria in high-order cases and propose a circulation mechanism to prevent the undesired equilibria on the boundary of the safe set. Finally, we validate our framework with three experiments on the Franka Research 3 robotic manipulator, demonstrating successful collision avoidance and the efficacy of the circulation mechanism.

eess.SY↗

arXiv2025

Data-Efficient System Identification via Lipschitz Neural Networks

Extracting dynamic models from data is of enormous importance in understanding the properties of unknown systems. In this work, we employ Lipschitz neural networks, a class of neural networks with a prescribed upper bound on their Lipschitz constant, to address the problem of data-efficient nonlinear system identification. Under the (fairly weak) assumption that the unknown system is Lipschitz continuous, we propose a method to estimate the approximation error bound of the trained network and the bound on the difference between the simulated trajectories by the trained models and the true system. Empirical results show that our method outperforms classic fully connected neural networks and Lipschitz regularized networks through simulation studies on three dynamical systems, and the advantage of our method is more noticeable when less data is used for training.

eess.SY↗

arXiv2025

SaMOSA: Sandbox for Malware Orchestration and Side-Channel Analysis

Cyber-attacks on operational technology (OT) and cyber-physical systems (CPS) have increased tremendously in recent years with the proliferation of malware targeting Linux-based embedded devices of OT and CPS systems. Comprehensive malware detection requires dynamic analysis of execution behavior in addition to static analysis of binaries. Safe execution of malware in a manner that captures relevant behaviors via side-channels requires a sandbox environment. Existing Linux sandboxes are built for specific tasks, only capture one or two side-channels, and do not offer customization for different analysis tasks. We present the SaMOSA Linux sandbox that allows emulation of Linux malwares while capturing time-synchronized side-channels from four sources. SaMOSA additionally provides emulation of network services via FakeNet, and allows orchestration and customization of the sandbox environment via pipeline hooks. In comparison to existing Linux sandboxes, SaMOSA captures more side-channels namely system calls, network activity, disk activity, and hardware performance counters. It supports three architectures predominantly used in OT and CPS namely x86-64, ARM64, and PowerPC 64. SaMOSA fills a gap in Linux malware analysis by providing a modular and customizable sandbox framework that can be adapted for many malware analysis tasks. We present three case studies of three different malware families to demonstrate the advantages of SaMOSA.

cs.CR↗

arXiv2025

EMMA: Efficient Visual Alignment in Multi-Modal LLMs

Multi-modal Large Language Models (MLLMs) have recently exhibited impressive general-purpose capabilities by leveraging vision foundation models to encode the core concepts of images into representations. These are then combined with instructions and processed by the language model to generate high-quality responses. Despite significant progress in enhancing the language component, challenges persist in optimally fusing visual encodings within the language model for task-specific adaptability. Recent research has focused on improving this fusion through modality adaptation modules but at the cost of significantly increased model complexity and training data needs. In this paper, we propose EMMA (Efficient Multi-Modal Adaptation), a lightweight cross-modality module designed to efficiently fuse visual and textual encodings, generating instruction-aware visual representations for the language model. Our key contributions include: (1) an efficient early fusion mechanism that integrates vision and language representations with minimal added parameters (less than 0.2% increase in model size), (2) an in-depth interpretability analysis that sheds light on the internal mechanisms of the proposed method; (3) comprehensive experiments that demonstrate notable improvements on both specialized and general benchmarks for MLLMs. Empirical results show that EMMA boosts performance across multiple tasks by up to 9.3% while significantly improving robustness against hallucinations. Our code is available at https://github.com/SaraGhazanfari/EMMA

cs.CV↗

arXiv2025

EnIGMA: Interactive Tools Substantially Assist LM Agents in Finding Security Vulnerabilities

Although language model (LM) agents have demonstrated increased performance in multiple domains, including coding and web-browsing, their success in cybersecurity has been limited. We present EnIGMA, an LM agent for autonomously solving Capture The Flag (CTF) challenges. We introduce new tools and interfaces to improve the agent's ability to find and exploit security vulnerabilities, focusing on interactive terminal programs. These novel Interactive Agent Tools enable LM agents, for the first time, to run interactive utilities, such as a debugger and a server connection tool, which are essential for solving these challenges. Empirical analysis on 390 CTF challenges across four benchmarks demonstrate that these new tools and interfaces substantially improve our agent's performance, achieving state-of-the-art results on NYU CTF, Intercode-CTF, and CyBench. Finally, we analyze data leakage, developing new methods to quantify it and identifying a new phenomenon we term soliloquizing, where the model self-generates hallucinated observations without interacting with the environment. Our code and development dataset are available at https://github.com/SWE-agent/SWE-agent/tree/v0.7 and https://github.com/NYU-LLM-CTF/NYU_CTF_Bench/tree/main/development respectively.

cs.AI↗

arXiv2025

Proactive Hierarchical Control Barrier Function-Based Safety Prioritization in Close Human-Robot Interaction Scenarios

In collaborative human-robot environments, the unpredictable and dynamic nature of human motion can lead to situations where collisions become unavoidable. In such cases, it is essential for the robotic system to proactively mitigate potential harm through intelligent control strategies. This paper presents a hierarchical control framework based on Control Barrier Functions (CBFs) designed to ensure safe and adaptive operation of autonomous robotic manipulators during close-proximity human-robot interaction. The proposed method introduces a relaxation variable that enables real-time prioritization of safety constraints, allowing the robot to dynamically manage collision risks based on the criticality of different parts of the human body. A secondary constraint mechanism is incorporated to resolve infeasibility by increasing the priority of imminent threats. The framework is experimentally validated on a Franka Research 3 robot equipped with a ZED2i AI camera for real-time human pose and body detection. Experimental results confirm that the CBF-based controller, integrated with depth sensing, facilitates responsive and safe human-robot collaboration, while providing detailed risk analysis and maintaining robust performance in highly dynamic settings.

cs.RO↗

arXiv2025

RAZER: Robust Accelerated Zero-Shot 3D Open-Vocabulary Panoptic Reconstruction with Spatio-Temporal Aggregation

Mapping and understanding complex 3D environments is fundamental to how autonomous systems perceive and interact with the physical world, requiring both precise geometric reconstruction and rich semantic comprehension. While existing 3D semantic mapping systems excel at reconstructing and identifying predefined object instances, they lack the flexibility to efficiently build semantic maps with open-vocabulary during online operation. Although recent vision-language models have enabled open-vocabulary object recognition in 2D images, they haven't yet bridged the gap to 3D spatial understanding. The critical challenge lies in developing a training-free unified system that can simultaneously construct accurate 3D maps while maintaining semantic consistency and supporting natural language interactions in real time. In this paper, we develop a zero-shot framework that seamlessly integrates GPU-accelerated geometric reconstruction with open-vocabulary vision-language models through online instance-level semantic embedding fusion, guided by hierarchical object association with spatial indexing. Our training-free system achieves superior performance through incremental processing and unified geometric-semantic updates, while robustly handling 2D segmentation inconsistencies. The proposed general-purpose 3D scene understanding framework can be used for various tasks including zero-shot 3D instance retrieval, segmentation, and object detection to reason about previously unseen objects and interpret natural language queries. The project page is available at https://razer-3d.github.io.

cs.CV↗

arXiv2025

CRAKEN: Cybersecurity LLM Agent with Knowledge-Based Execution

Large Language Model (LLM) agents can automate cybersecurity tasks and can adapt to the evolving cybersecurity landscape without re-engineering. While LLM agents have demonstrated cybersecurity capabilities on Capture-The-Flag (CTF) competitions, they have two key limitations: accessing latest cybersecurity expertise beyond training data, and integrating new knowledge into complex task planning. Knowledge-based approaches that incorporate technical understanding into the task-solving automation can tackle these limitations. We present CRAKEN, a knowledge-based LLM agent framework that improves cybersecurity capability through three core mechanisms: contextual decomposition of task-critical information, iterative self-reflected knowledge retrieval, and knowledge-hint injection that transforms insights into adaptive attack strategies. Comprehensive evaluations with different configurations show CRAKEN's effectiveness in multi-stage vulnerability detection and exploitation compared to previous approaches. Our extensible architecture establishes new methodologies for embedding new security knowledge into LLM-driven cybersecurity agentic systems. With a knowledge database of CTF writeups, CRAKEN obtained an accuracy of 22% on NYU CTF Bench, outperforming prior works by 3% and achieving state-of-the-art results. On evaluation of MITRE ATT&CK techniques, CRAKEN solves 25-30% more techniques than prior work, demonstrating improved cybersecurity capabilities via knowledge-based execution. We make our framework open source to public https://github.com/NYU-LLM-CTF/nyuctf_agents_craken.

cs.CR↗

arXiv2025

D-CIPHER: Dynamic Collaborative Intelligent Multi-Agent System with Planner and Heterogeneous Executors for Offensive Security

Large Language Models (LLMs) have been used in cybersecurity such as autonomous security analysis or penetration testing. Capture the Flag (CTF) challenges serve as benchmarks to assess automated task-planning abilities of LLM agents for cybersecurity. Early attempts to apply LLMs for solving CTF challenges used single-agent systems, where feedback was restricted to a single reasoning-action loop. This approach was inadequate for complex CTF tasks. Inspired by real-world CTF competitions, where teams of experts collaborate, we introduce the D-CIPHER LLM multi-agent framework for collaborative CTF solving. D-CIPHER integrates agents with distinct roles with dynamic feedback loops to enhance reasoning on complex tasks. It introduces the Planner-Executor agent system, consisting of a Planner agent for overall problem-solving along with multiple heterogeneous Executor agents for individual tasks, facilitating efficient allocation of responsibilities among the agents. Additionally, D-CIPHER incorporates an Auto-prompter agent to improve problem-solving by auto-generating a highly relevant initial prompt. We evaluate D-CIPHER on multiple CTF benchmarks and LLM models via comprehensive studies to highlight the impact of our enhancements. Additionally, we manually map the CTFs in NYU CTF Bench to MITRE ATT&CK techniques that apply for a comprehensive evaluation of D-CIPHER's offensive security capability. D-CIPHER achieves state-of-the-art performance on three benchmarks: 22.0% on NYU CTF Bench, 22.5% on Cybench, and 44.0% on HackTheBox, which is 2.5% to 8.5% better than previous work. D-CIPHER solves 65% more ATT&CK techniques compared to previous work, demonstrating stronger offensive capability.

cs.AI↗