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Found 9 papers, 3 papers with code
Testing and Understanding Erroneous Planning in LLM Agents through Synthesized User Inputs
These synthesized inputs are natural language paragraphs that specify the requirements for completing a series of tasks.
LLMs Can Defend Themselves Against Jailbreaking in a Practical Manner: A Vision Paper
Our key insight is that regardless of the kind of jailbreak strategies employed, they eventually need to include a harmful prompt (e. g., "how to make a bomb") in the prompt sent to LLMs, and we found that existing LLMs can effectively recognize such harmful prompts that violate their safety policies.
LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning
Large language models (LLMs) have demonstrated significant poten- tial for many downstream tasks, including those requiring human- level intelligence, such as vulnerability detection.
VRPTEST: Evaluating Visual Referring Prompting in Large Multimodal Models
With recent advancements in Large Multimodal Models (LMMs) across various domains, a novel prompting method called visual referring prompting has emerged, showing significant potential in enhancing human-computer interaction within multimodal systems.
Split and Merge: Aligning Position Biases in Large Language Model based Evaluators
Specifically, PORTIA splits the answers into multiple segments, aligns similar content across candidate answers, and then merges them back into a single prompt for evaluation by LLMs.
GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysis
Instead of relying solely on GPT to identify vulnerabilities, which can lead to high false positives and is limited by GPT's pre-trained knowledge, we utilize GPT as a versatile code understanding tool.
iExam: A Novel Online Exam Monitoring and Analysis System Based on Face Detection and Recognition
In this paper, we propose iExam, an intelligent online exam monitoring and analysis system that can not only use face detection to assist invigilators in real-time student identification, but also be able to detect common abnormal behaviors (including face disappearing, rotating faces, and replacing with a different person during the exams) via a face recognition-based post-exam video analysis.
A Sink-driven Approach to Detecting Exposed Component Vulnerabilities in Android Apps
We implement our sink-driven approach in a tool called ECVDetector and evaluate it with the top 1K Android apps.
Cryptography and Security
Analyzing Android Browser Apps for file:// Vulnerabilities
We design an automated system to dynamically test 115 browser apps collected from Google Play and find that 64 of them are vulnerable to the attacks.
Cryptography and Security
