36 citations · 93 across the 25 of their papers we have counts for
8 papers · 1 filter
NetGuise: Adversarial Traffic Generation for Early-Stage Classifier Evasion
Binghui Wu, Dinil Mon Divakaran, Mohan Gurusamy
Early-stage encrypted traffic classifiers make decisions from only the first few packets of a connection, enabling low-latency classification but raising robustness concerns under…
DarKnight: Retrieval-Guided Agentic Framework for Breaking Dark Web CAPTCHA Gates
Binghui Wu, Yeo Zi Yi, Darren Tan +3
Cyber Threat Intelligence (CTI) investigators who monitor dark web marketplaces for leaked credentials and breach listings must pass the diverse CAPTCHAs that operators deploy to b…
The Fragility of Trigger-Tag Mechanisms for Misuse Detection in Open-Weight LLMs
Toluwani Aremu, Manit Baser, Mohan Gurusamy +2
Open-weight language models can be downloaded, modified, and deployed beyond their developers' control, limiting the effectiveness of centrally enforced safeguards. Recent work has…
The Tokens Remember: When Tokenization Bypasses Knowledge Editing and Unlearning
Manit Baser, Aditya Nawal, Dinil Mon Divakaran +1
Open-weight LLMs give downstream users control over the inference stack, but this flexibility can undermine post-release guarantees that sensitive knowledge has been modified or re…
Drishti: AI-Led Human-Directed Vulnerability Auditing for 5G Cores
Sriram Ramachandran, Levente Csikor, Dinil Mon Divakaran
Candidate generation for open-source vulnerabilities is no longer scarce. AI-assisted code review now produces defect candidates cheaply, and industry programs pair them with exper…
CHISEL-ing Back Source Code with AI-enabled Iterative Recovery
Varun Kohli, N Raghava, Biplab Sikdar +1
Decompilation aims to recover high-level, compilable, and semantically equivalent code from binaries. Traditional decompilers produce pseudo-C that is difficult to read and does no…
MATRA: Modeling the Attack Surface of Agentic AI Systems -- OpenClaw Case Study
Tim Van hamme, Thomas Vissers, Javier Carnerero-Cano +4
LLMs are increasingly deployed as autonomous agents with access to tools, databases, and external services, yet practitioners (across different sectors) lack systematic methods to…
CLaRE-ty Amid Chaos: Quantifying Representational Entanglement to Predict Ripple Effects in LLM Editing
Manit Baser, Alperen Yildiz, Dinil Mon Divakaran +1
The static knowledge representations of large language models (LLMs) inevitably become outdated or incorrect over time. While model-editing techniques offer a promising solution by…