9 citations · 11 across the 11 of their papers we have counts for
13 papers
RupeeBias: Auditing Demographic Bias in Indian Economic Guidance from Large Language Models
Pavithra P M Nair, Bhavik Talaviya, Shourya Bhushan +5
Individuals turn to large language models (LLMs) for guidance across a wide range of economic tasks, from comparing loan options and planning savings to deciding what raise to ask…
Beyond In-Distribution Metrics: A Systematic Out-of-Distribution Evaluation of Congenital Heart Disease Segmentation
Aniketh Vijesh, Shrisharanyan Vasu, Abhijit Ramesh +5
Congenital heart disease (CHD) diagnosis and surgical planning often require patient-specific 3D anatomical models, but manual segmentation is labor-intensive, particularly in comp…
PRISM: Recovering Instruction Sets from Language Model Activations
Gilad Gressel, Rahul Pankajakshan, Julia Diament +3
As LLMs are deployed as agents, reliable monitoring requires knowing not only what they output, but which instructions are steering their behavior. This is difficult when models in…
A Few Steps Further: Why Defenses Against Malicious Finetuning Erode Under Continued Training
Itay Zloczower, Eyal Lenga, Gilad Gressel +1
Model providers increasingly release the weights of large language models. Although these models are safety-aligned before release, their safeguards can often be removed by fine-tu…
Who Owns This Agent? Tracing AI Agents Back to Their Owners
Ruben Chocron, Doron Jonathan Ben Chayim, Eyal Lenga +3
AI agents increasingly act autonomously in the world, yet harmful behavior cannot be reliably traced to the account that deployed the agent. This creates an accountability gap acro…
GAVEL: Towards Rule-Based Safety Through Activation Monitoring
Shir Rozenfeld, Rahul Pankajakshan, Itay Zloczower +3
Large language models (LLMs) are increasingly paired with activation-based monitoring to detect and prevent harmful behaviors that may not be apparent at the surface-text level. Ho…
Love, Lies, and Language Models: Investigating AI's Role in Romance-Baiting Scams
Gilad Gressel, Rahul Pankajakshan, Shir Rozenfeld +4
Romance-baiting scams have become a major source of financial and emotional harm worldwide. These operations are run by organized crime syndicates that traffic thousands of people…
ProxyPrints: From Database Breach to Spoof, A Plug-and-Play Defense for Biometric Systems
Yaniv Hacmon, Keren Gorelik, Gilad Gressel +1
Fingerprint recognition systems are widely deployed for authentication and forensic applications, but the security of stored fingerprint data remains a critical vulnerability. Whil…
ASRJam: Human-Friendly AI Speech Jamming to Prevent Automated Phone Scams
Freddie Grabovski, Gilad Gressel, Yisroel Mirsky
Large Language Models (LLMs), combined with Text-to-Speech (TTS) and Automatic Speech Recognition (ASR), are increasingly used to automate voice phishing (vishing) scams. These sys…
Are You Human? An Adversarial Benchmark to Expose LLMs
Gilad Gressel, Rahul Pankajakshan, Yisroel Mirsky
Large Language Models (LLMs) have demonstrated an alarming ability to impersonate humans in conversation, raising concerns about their potential misuse in scams and deception. Huma…
Mapping LLM Security Landscapes: A Comprehensive Stakeholder Risk Assessment Proposal
Rahul Pankajakshan, Sumitra Biswal, Yuvaraj Govindarajulu +1
The rapid integration of Large Language Models (LLMs) across diverse sectors has marked a transformative era, showcasing remarkable capabilities in text generation and problem-solv…
Automatic Endoscopic Ultrasound Station Recognition with Limited Data
Abhijit Ramesh, Anantha Nandanan, Nikhil Boggavarapu +2
Pancreatic cancer is a lethal form of cancer that significantly contributes to cancer-related deaths worldwide. Early detection is essential to improve patient prognosis and surviv…
Feature Importance Guided Attack: A Model Agnostic Adversarial Attack
Gilad Gressel, Niranjan Hegde, Archana Sreekumar +4
Research in adversarial learning has primarily focused on homogeneous unstructured datasets, which often map into the problem space naturally. Inverting a feature space attack on h…