17 citations · 19 across the 10 of their papers we have counts for
10 papers
Agentic Adversarial Rewriting Exposes Architectural Vulnerabilities in Black-Box NLP Pipelines
Mazal Bethany, Kim-Kwang Raymond Choo, Nishant Vishwamitra +1
Multi-component natural language processing (NLP) pipelines are increasingly deployed for high-stakes decisions, yet no existing adversarial method can test their robustness under…
Reflective Agreement: Combining Self-Mixture of Agents with a Sequence Tagger for Robust Event Extraction
Fatemeh Haji, Mazal Bethany, Cho-Yu Jason Chiang +2
Event Extraction (EE) involves automatically identifying and extracting structured information about events from unstructured text, including triggers, event types, and arguments.…
CAMOUFLAGE: Exploiting Misinformation Detection Systems Through LLM-driven Adversarial Claim Transformation
Mazal Bethany, Nishant Vishwamitra, Cho-Yu Jason Chiang +1
Automated evidence-based misinformation detection systems, which evaluate the veracity of short claims against evidence, lack comprehensive analysis of their adversarial vulnerabil…
Improving LLM Reasoning with Multi-Agent Tree-of-Thought Validator Agent
Fatemeh Haji, Mazal Bethany, Maryam Tabar +3
Multi-agent strategies have emerged as a promising approach to enhance the reasoning abilities of Large Language Models (LLMs) by assigning specialized roles in the problem-solving…
Jailbreaking Large Language Models with Symbolic Mathematics
Emet Bethany, Mazal Bethany, Juan Arturo Nolazco Flores +2
Recent advancements in AI safety have led to increased efforts in training and red-teaming large language models (LLMs) to mitigate unsafe content generation. However, these safety…
Enhancing Event Reasoning in Large Language Models through Instruction Fine-Tuning with Semantic Causal Graphs
Mazal Bethany, Emet Bethany, Brandon Wherry +4
Event detection and text reasoning have become critical applications across various domains. While LLMs have recently demonstrated impressive progress in reasoning abilities, they…
Unintentional Security Flaws in Code: Automated Defense via Root Cause Analysis
Nafis Tanveer Islam, Mazal Bethany, Dylan Manuel +2
Software security remains a critical concern, particularly as junior developers, often lacking comprehensive knowledge of security practices, contribute to codebases. While there a…
Image Safeguarding: Reasoning with Conditional Vision Language Model and Obfuscating Unsafe Content Counterfactually
Mazal Bethany, Brandon Wherry, Nishant Vishwamitra +1
Social media platforms are being increasingly used by malicious actors to share unsafe content, such as images depicting sexual activity, cyberbullying, and self-harm. Consequently…
Lateral Phishing With Large Language Models: A Large Organization Comparative Study
Mazal Bethany, Athanasios Galiopoulos, Emet Bethany +4
The emergence of Large Language Models (LLMs) has heightened the threat of phishing emails by enabling the generation of highly targeted, personalized, and automated attacks. Tradi…
Deciphering Textual Authenticity: A Generalized Strategy through the Lens of Large Language Semantics for Detecting Human vs. Machine-Generated Text
Mazal Bethany, Brandon Wherry, Emet Bethany +3
With the recent proliferation of Large Language Models (LLMs), there has been an increasing demand for tools to detect machine-generated text. The effective detection of machine-ge…