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Researcher profile

Dinil Mon Divakaran

Dinil Mon Divakaran contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Cryptography and SecurityMachine LearningArtificial IntelligenceNetworking and Internet Architecture

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Published work

6 published item(s)

preprint2026arXiv

MATRA: Modeling the Attack Surface of Agentic AI Systems -- OpenClaw Case Study

LLMs are increasingly deployed as autonomous agents with access to tools, databases, and external services, yet practitioners (across different sectors) lack systematic methods to assess how known threat classes translate into concrete risks within a specific agentic deployment. We present MATRA, a pragmatic threat modeling framework for agentic AI systems that adapts established risk assessment methodology to systematically assess how known LLM threats translate into deployment-specific risks. MATRA begins with an asset-based impact assessment and utilizes attack trees to determine the likelihood of these impacts occurring within the system architecture. We demonstrate MATRA on a personal AI agent deployment using OpenClaw, quantifying how architectural controls such as network sandboxing and least-privilege access reduce risk by limiting the blast radius of successful injections.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

ThinkEval: Practical Evaluation of Knowledge Leakage in LLM Editing using Thought-based Knowledge Graphs

Robust model-editing techniques are essential for deploying large language models (LLMs) in practical applications, as they enable cost-effective ways to deal with challenges such as privacy breaches, bias mitigation and misinformation spread. For example, an LLM-based healthcare assistance may need to update out-dated or incorrect knowledge to prevent harmful recommendations. However, many editing techniques focus on isolated facts, which critically fail to prevent indirect knowledge leakage -- the unintended reconstruction of edited-out information through persistent causal links and contextual relationships. To assist users in selecting the right editing technique, we develop and present ThinkEval, a framework to systematically quantify indirect knowledge leakage and ripple effects in model-editing. ThinkEval builds and employs specialized knowledge graphs to analyze the causal structure of facts before and after editing. To support this approach, we present KnowGIC, a benchmark dataset comprising multi-step reasoning paths that precisely measure these complex knowledge transformation effects. We evaluate five editing techniques: AlphaEdit, RECT, ROME, MEMIT, and PRUNE across multiple LLMs. Our results show that these techniques struggle to balance indirect fact suppression with the preservation of related knowledge, compromising the contextual integrity of a model's knowledge. Our dataset is available at: https://github.com/manitbaser/KnowGIC.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten

As the use of machine learning (ML) models is becoming increasingly popular in many real-world applications, there are practical challenges that need to be addressed for model maintenance. One such challenge is to 'undo' the effect of a specific subset of dataset used for training a model. This specific subset may contain malicious or adversarial data injected by an attacker, which affects the model performance. Another reason may be the need for a service provider to remove data pertaining to a specific user to respect the user's privacy. In both cases, the problem is to 'unlearn' a specific subset of the training data from a trained model without incurring the costly procedure of retraining the whole model from scratch. Towards this goal, this paper presents a Markov chain Monte Carlo-based machine unlearning (MCU) algorithm. MCU helps to effectively and efficiently unlearn a trained model from subsets of training dataset. Furthermore, we show that with MCU, we are able to explain the effect of a subset of a training dataset on the model prediction. Thus, MCU is useful for examining subsets of data to identify the adversarial data to be removed. Similarly, MCU can be used to erase the lineage of a user's personal data from trained ML models, thus upholding a user's "right to be forgotten". We empirically evaluate the performance of our proposed MCU algorithm on real-world phishing and diabetes datasets. Results show that MCU can achieve a desirable performance by efficiently removing the effect of a subset of training dataset and outperform an existing algorithm that utilizes the remaining dataset.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

SIERRA: Ranking Anomalous Activities in Enterprise Networks

An enterprise today deploys multiple security middleboxes such as firewalls, IDS, IPS, etc. in its network to collect different kinds of events related to threats and attacks. These events are streamed into a SIEM (Security Information and Event Management) system for analysts to investigate and respond quickly with appropriate actions. However, the number of events collected for a single enterprise can easily run into hundreds of thousands per day, much more than what analysts can investigate under a given budget constraint (time). In this work, we look into the problem of prioritizing suspicious events or anomalies to analysts for further investigation. We develop SIERRA, a system that processes event logs from multiple and diverse middleboxes to detect and rank anomalous activities. SIERRA takes an unsupervised approach and therefore has no dependence on ground truth data. Different from other works, SIERRA defines contexts, that help it to provide visual explanations of highly-ranked anomalous points to analysts, despite employing unsupervised models. We evaluate SIERRA using months of logs from multiple security middleboxes of an enterprise network. The evaluations demonstrate the capability of SIERRA to detect top anomalies in a network while outperforming naive application of existing anomaly detection algorithms as well as a state-of-the-art SIEM-based anomaly detection solution.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

A Survey of Privacy-Preserving Techniques for Encrypted Traffic Inspection over Network Middleboxes

Middleboxes in a computer network system inspect and analyse network traffic to detect malicious communications, monitor system performance and provide operational services. However, encrypted traffic hinders the ability of middleboxes to perform such services. A common practice in addressing this issue is by employing a "Man-in-the-Middle" (MitM) approach, wherein an encrypted traffic flow between two endpoints is interrupted, decrypted and analysed by the middleboxes. The MitM approach is straightforward and is used by many organisations, but there are both practical and privacy concerns. Due to the cost of the MitM appliances and the latency incurred in the encrypt-decrypt processes, enterprises continue to seek solutions that are less costly. There were discussion on the many efforts required to configure MitM. Besides, MitM violates end-to-end privacy guarantee, raising privacy concerns and issues on compliance especially with the rising awareness on user privacy. Furthermore, some of the MitM implementations were found to be flawed. Consequently, new practical and privacy-preserving techniques for inspection over encrypted traffic were proposed. We examine them to compare their advantages, limitations and challenges. We categorise them into four main categories by defining a framework that consist of system architectures, use cases, trust and threat models. These are searchable encryption, access control, machine learning and trusted hardware. We first discuss the man-in-the-middle approach as a baseline, then discuss in details each of them, and provide an in-depth comparisons of their advantages and limitations. By doing so we describe practical constraints, advantages and pitfalls towards adopting the techniques. We also give insights on the gaps between research work and industrial deployment, which leads us to the discussion on the challenges and research directions.

0 citations0 reviewsNo code linkedOpen access