11 citations · 13 across the 6 of their papers we have counts for
6 papers
Verifying Intent and Harm: A Unified Defense Against LLM-Generated Threats
Poojitha Thota, Yun Lei, Santhosh Thangaraj +2
Large language models (LLMs) are increasingly deployed in interactive applications, yet they remain vulnerable to adversarial interactions that induce harmful, deceptive, or policy…
Detect, Unlearn, Restore: Defending Text Summarization Models Against Data Poisoning
Poojitha Thota, Shirin Nilizadeh
Training-time data poisoning during fine-tuning poses a significant threat to large language models (LLMs) deployed for abstractive text summarization, where small task-specific da…
FairDeFace: Evaluating the Fairness and Adversarial Robustness of Face Obfuscation Methods
Seyyed Mohammad Sadegh Moosavi Khorzooghi, Poojitha Thota, Mohit Singhal +3
The lack of a common platform and benchmark datasets for evaluating face obfuscation methods has been a challenge, with every method being tested using arbitrary experiments, datas…
Attacks against Abstractive Text Summarization Models through Lead Bias and Influence Functions
Poojitha Thota, Shirin Nilizadeh
Large Language Models have introduced novel opportunities for text comprehension and generation. Yet, they are vulnerable to adversarial perturbations and data poisoning attacks, p…
Demonstration of an Adversarial Attack Against a Multimodal Vision Language Model for Pathology Imaging
Poojitha Thota, Jai Prakash Veerla, Partha Sai Guttikonda +3
In the context of medical artificial intelligence, this study explores the vulnerabilities of the Pathology Language-Image Pretraining (PLIP) model, a Vision Language Foundation mo…
From Chatbots to PhishBots? -- Preventing Phishing scams created using ChatGPT, Google Bard and Claude
Sayak Saha Roy, Poojitha Thota, Krishna Vamsi Naragam +1
The advanced capabilities of Large Language Models (LLMs) have made them invaluable across various applications, from conversational agents and content creation to data analysis, r…