arXiv subjects

Abdul Serwadda

Publications and source records attributed to Abdul Serwadda.

8 recordsLinked to original sources

arXiv2026

Vibe Coding on Trial: Operating Characteristics of Unanimous LLM Juries

Large Language Models (LLMs) are now good enough at coding that developers can describe intent in plain language and let the tool produce the first code draft, a workflow increasingly built into tools like GitHub Copilot, Cursor, and Replit. What is missing is a reliable way to tell which model written queries are safe to accept without sending everything to a human. We study the application of an LLM jury to run this review step. We first benchmark 15 open models on 82 MySQL text to SQL tasks using an execution grounded protocol to get a clean baseline of which models are strong. From the six best models we build unanimous committees of sizes 1 through 6 that see the prompt, schema, and candidate SQL and accept it only when every member says it is correct. This rule matches safety first deployments where false accepts are more costly than false rejects. We measure true positive rate, false positive rate and Youden J and we also look at committees per generator. Our results show that single model judges are uneven, that small unanimous committees of strong models can cut false accepts while still passing many good queries, and that the exact committee composition matters significantly.

cs.DB↗

arXiv2026

Intermittent File Encryption in Ransomware: Measurement, Modeling, and Detection

File-encrypting ransomware increasingly employs intermittent encryption techniques, encrypting only parts of files to evade classical detection methods.This paper provides a systematic empirical characterization of byte-level statistics under intermittent encryption across common file types, establishing a baseline for how partial encryption reshapes data structure. Guided by these measurements, we model intermittent encryption as a convex mixture of ciphertext and cleartext and, via a classical KL-divergence bound, derive file-type-specific detectability limits for histogram-based detectors. Leveraging these insights, we evaluate convolutional neural network (CNN) detectors trained on realistic intermittent-encryption configurations from leading ransomware families. Our findings show that localized, chunk-level CNNs consistently outperform whole-file analysis, highlighting a practical, robust baseline for future detection systems.

cs.CR↗

arXiv2026

Beyond the Voice: Inertial Sensing of Mouth Motion for High Security Speech Verification

Voice interfaces are increasingly used in high-stakes domains such as mobile banking, smart-home security, and hands-free healthcare. Meanwhile, modern generative models have made high-quality voice forgeries inexpensive and easy to create, eroding confidence in voice authentication alone. To strengthen protection against such attacks, we present a second authentication factor that combines acoustic evidence with the unique motion patterns of a speaker's lower face. By placing lightweight inertial sensors around the mouth to capture mouth opening and evolving lower-facial geometry, our system records a distinct motion signature with strong discriminative power across individuals. We built a prototype and recruited 43 participants to evaluate the system under four conditions: seated, walking on level ground, walking on stairs, and speaking with different language backgrounds (native vs. non-native English). Across all scenarios, our approach consistently achieved a median equal-error rate (EER) of 0.01 or lower, indicating that mouth-movement data remain robust under variations in gait, posture, and spoken language. We discuss specific use cases where this second line of defense could provide tangible security benefits to voice authentication systems.

cs.CR↗

arXiv2025

Guarding Your Conversations: Privacy Gatekeepers for Secure Interactions with Cloud-Based AI Models

The interactive nature of Large Language Models (LLMs), which closely track user data and context, has prompted users to share personal and private information in unprecedented ways. Even when users opt out of allowing their data to be used for training, these privacy settings offer limited protection when LLM providers operate in jurisdictions with weak privacy laws, invasive government surveillance, or poor data security practices. In such cases, the risk of sensitive information, including Personally Identifiable Information (PII), being mishandled or exposed remains high. To address this, we propose the concept of an "LLM gatekeeper", a lightweight, locally run model that filters out sensitive information from user queries before they are sent to the potentially untrustworthy, though highly capable, cloud-based LLM. Through experiments with human subjects, we demonstrate that this dual-model approach introduces minimal overhead while significantly enhancing user privacy, without compromising the quality of LLM responses.

cs.CR↗

arXiv2024

Exploiting HDMI and USB Ports for GPU Side-Channel Insights

Modern computers rely on USB and HDMI ports for connecting external peripherals and display devices. Despite their built-in security measures, these ports remain susceptible to passive power-based side-channel attacks. This paper presents a new class of attacks that exploit power consumption patterns at these ports to infer GPU activities. We develop a custom device that plugs into these ports and demonstrate that its high-resolution power measurements can drive successful inferences about GPU processes, such as neural network computations and video rendering. The ubiquitous presence of USB and HDMI ports allows for discreet placement of the device, and its non-interference with data channels ensures that no security alerts are triggered. Our findings underscore the need to reevaluate and strengthen the current generation of HDMI and USB port security defenses.

cs.CR↗

arXiv2024

Unmasking the giant: A comprehensive evaluation of ChatGPT's proficiency in coding algorithms and data structures

The transformative influence of Large Language Models (LLMs) is profoundly reshaping the Artificial Intelligence (AI) technology domain. Notably, ChatGPT distinguishes itself within these models, demonstrating remarkable performance in multi-turn conversations and exhibiting code proficiency across an array of languages. In this paper, we carry out a comprehensive evaluation of ChatGPT's coding capabilities based on what is to date the largest catalog of coding challenges. Our focus is on the python programming language and problems centered on data structures and algorithms, two topics at the very foundations of Computer Science. We evaluate ChatGPT for its ability to generate correct solutions to the problems fed to it, its code quality, and nature of run-time errors thrown by its code. Where ChatGPT code successfully executes, but fails to solve the problem at hand, we look into patterns in the test cases passed in order to gain some insights into how wrong ChatGPT code is in these kinds of situations. To infer whether ChatGPT might have directly memorized some of the data that was used to train it, we methodically design an experiment to investigate this phenomena. Making comparisons with human performance whenever feasible, we investigate all the above questions from the context of both its underlying learning models (GPT-3.5 and GPT-4), on a vast array sub-topics within the main topics, and on problems having varying degrees of difficulty.

cs.SE↗

arXiv2022

A Wearables-Driven Attack on Examination Proctoring

Multiple choice questions are at the heart of many standardized tests and examinations at academic institutions allover the world. In this paper, we argue that recent advancements in sensing and human-computer interaction expose these types of questions to highly effective attacks that today's proctor's are simply not equipped to detect. We design one such attack based on a protocol of carefully orchestrated wrist movements combined with haptic and visual feedback mechanisms designed for stealthiness. The attack is done through collaboration between a knowledgeable student (i.e., a mercenary) and a weak student (i.e., the beneficiary) who depends on the mercenary for solutions. Through a combination of experiments and theoretical modeling, we show the attack to be highly effective. The paper makes the case for an outright ban on all tech gadgets inside examination rooms, irrespective of whether their usage appears benign to the plain eye.

cs.CR↗

arXiv2019

Kinetic Song Comprehension: Deciphering Personal Listening Habits via Phone Vibrations

Music is an expression of our identity, showing a significant correlation with other personal traits, beliefs, and habits. If accessed by a malicious entity, an individual's music listening habits could be used to make critical inferences about the user. In this paper, we showcase an attack in which the vibrations propagated through a user's phone while playing music via its speakers can be used to detect and classify songs. Our attack shows that known songs can be detected with an accuracy of just under 80%, while a corpus of 100 songs can be classified with an accuracy greater than 80%. We investigate such questions under a wide variety of experimental scenarios involving three surfaces and five phone speaker volumes. Although users can mitigate some of the risk by using a phone cover to dampen the vibrations, we show that a sophisticated attacker could adapt the attack to still classify songs with a decent accuracy. This paper demonstrates a new way in which motion sensor data can be leveraged to intrude on user music preferences without their express permission. Whether this information is leveraged for financial gain or political purposes, our research makes a case for why more rigorous methods of protecting user data should be utilized by companies, and if necessary, individuals.

cs.CR↗