arXiv subjects

Raja Hasnain Anwar

Publications and source records attributed to Raja Hasnain Anwar.

5 recordsLinked to original sources

arXiv2026

Beyond the Quantum Promise: A Security Analysis of Classical Control in Quantum Key Distribution

Quantum Key Distribution (QKD) protocols provide information-theoretic security by using quantum mechanical principles. Yet QKD is fundamentally a hybrid protocol: its security depends on the correct integration of the quantum phase with classical post-processing. While ETSI and ITUT specifications standardize QKD architectures and interfaces, they evaluate protocol security in isolation, leaving cross-layer interactions as an underexplored attack surface. This paper introduces a formal verification framework that holistically models QKD protocols based on ETSI and ITUT QKD specifications. Our model is the first hybrid QKD protocol model that supports automated analysis of protocollevel security focusing on how classical operations influence the security guarantees provided by the quantum phase of the QKD protocol. We formalize a comprehensive symbolic model of QKD protocols, based on ETSI and ITU-T QKD specifications, in Tamarin, an automated protocol verifier. Applying this framework, we obtain formal evidence of three specification-level vulnerabilities in ETSI- and ITU-T-grounded protocol models under adversary Eve+: subverted entanglement injection, basis-deferred measurement, and message reflection. Each arises from a classical control-plane omission in the procedure text and is established under a symbolic abstraction rather than as a claim about all practical deployments. We introduce two protocol improvements: measurement commitment and identitybound message authentication codes (MACs). Tamarin verification confirms that these countermeasures eliminate the identified vulnerabilities under Eve+. We have communicated our results and recommendations to relevant standardization organizations.

quant-ph↗

arXiv2026

InferNet: Exploiting Aggregate GPU Profiles as Side-Channel for DNN Architecture Inference

Deep Neural Networks (DNNs) have become ubiquitous for their ability to solve problems across various domains, including computer vision, natural language processing, and speech recognition. However, as their adoption grows, they face a range of security threats, such as model stealing, architecture extraction, and manipulation, which can compromise their integrity, privacy, and functionality. Past works have relied on complex, fine-grained, and time-series analysis to launch DNN model extraction attacks. These approaches require extensive amounts of data, which are often challenging to acquire and analyze effectively. This paper introduces InferNet, an attack method that leverages simple, non-intrusive, and coarse-grained system-level information to identify the underlying DNN architecture of a victim's application. By analyzing GPU kernel calls, memory events, and system-level metrics, InferNet fingerprints the DNN and infers its architecture with very high accuracy. It can predict the architecture family (e.g., Inception vs. BERT), as well as the architecture variant (e.g., InceptionV1 vs. InceptionV3). The evaluation results demonstrate the effectiveness of InferNet across AI/ML frameworks (TensorFlow, PyTorch), different DNN types (vision, LLMs), and hardware platforms (NVIDIA Tesla T4, NVIDIA Quadro RTX 8000). The results show that InferNet achieves 100% model extraction accuracy using only a partial GPU profile under various attack settings.

cs.LG↗

arXiv2026

PPT-Eval: A Benchmark for Computer-Use Agents on PowerPoint Tasks

Creating and editing slides is a rich, multimodal activity that is ubiquitous in professional and educational settings, making it an ideal testbed for real-world computer-use agents. Microsoft PowerPoint is among the most widely adopted and feature-rich environments for presentation creation. We introduce PPT-Eval, a benchmark of 120 PowerPoint tasks across 12 files that cover both content creation and presentation editing scenarios, organized by difficulty. A central challenge in this domain is evaluation: tasks are complex, multimodal, and often admit many valid solutions. Moreover, today's agents frequently make only partial progress, which binary success metrics fail to capture. To address this, we design a robust evaluation framework to help create task-specific rubrics for PowerPoint tasks, taking inspiration from and building on past works for rubric-based evaluation. These rubrics award partial credit for intermediate steps, penalize unnecessary changes and poor aesthetics, and provide natural language feedback. This nuanced approach proves highly effective, achieving a Kendall's τ-b correlation of 0.77 with human judgments. We find that existing frontier agents still struggle with solving PowerPoint tasks, with strong models like Claude-4.5-Opus achieving only a 45% success rate and an average partial score of 57%. The benchmark is located at: https://microsoft.github.io/ppteval.

cs.LG↗

arXiv2025

VerificAgent: Domain-Specific Memory Verification for Scalable Oversight of Aligned Computer-Use Agents

Continual memory augmentation lets computer-using agents (CUAs) learn from prior interactions, but unvetted memories can encode domain-inappropriate or unsafe heuristics--spurious rules that drift from user intent and safety constraints. We introduce VerificAgent, a scalable oversight framework that treats persistent memory as an explicit alignment surface. VerificAgent combines (1) an expert-curated seed of domain knowledge, (2) iterative, trajectory-based memory growth during training, and (3) a post-hoc human fact-checking pass to sanitize accumulated memories before deployment. Evaluated on OSWorld productivity tasks and additional adversarial stress tests, VerificAgent improves task reliability, reduces hallucination-induced failures, and preserves interpretable, auditable guidance--without additional model fine-tuning. By letting humans correct high-impact errors once, the verified memory acts as a frozen safety contract that future agent actions must satisfy. Our results suggest that domain-scoped, human-verified memory offers a scalable oversight mechanism for CUAs, complementing broader alignment strategies by limiting silent policy drift and anchoring agent behavior to the norms and safety constraints of the target domain.

cs.LG↗

arXiv2024

Characterizing Encrypted Application Traffic through Cellular Radio Interface Protocol

Modern applications are end-to-end encrypted to prevent data from being read or secretly modified. 5G tech nology provides ubiquitous access to these applications without compromising the application-specific performance and latency goals. In this paper, we empirically demonstrate that 5G radio communication becomes the side channel to precisely infer the user's applications in real-time. The key idea lies in observing the 5G physical and MAC layer interactions over time that reveal the application's behavior. The MAC layer receives the data from the application and requests the network to assign the radio resource blocks. The network assigns the radio resources as per application requirements, such as priority, Quality of Service (QoS) needs, amount of data to be transmitted, and buffer size. The adversary can passively observe the radio resources to fingerprint the applications. We empirically demonstrate this attack by considering four different categories of applications: online shopping, voice/video conferencing, video streaming, and Over-The-Top (OTT) media platforms. Finally, we have also demonstrated that an attacker can differentiate various types of applications in real-time within each category.

cs.NI↗