3 citations · 3 across the 7 of their papers we have counts for
11 papers
Ulysses: A Security Harness For Synthesizing Verifiable Web Exploits
Aditya Oke, André Vicente Duarte, Rui Melo +6
How can an AI agent automatically discover security vulnerabilities in any software repository? Existing approaches use LLMs to generate software vulnerability exploits. However, i…
Self-Evolving Static Analysis via LLM Pseudo-Labeling
Rui Melo, Andre Duarte, Aditya Oke +5
Security bugs often slip past static analysis not because they are impossible to detect, but because detection rules have not yet been written for the code in front of us. New fram…
ABSENTIA: Detecting Broken Access Control Vulnerabilities in Web Applications
André V. Duarte, Aditya Oke, Rui Melo +7
Broken access control, the failure of authorization, is one of the most prevalent web security risks. Unlike injection, a flow of untrusted input into a dangerous operation, author…
AuraForge: Scaling Security Supervision for Training Coding Agents
Danqing Wang, Songwen Zhao, Harsh Sharma +4
Coding agents are now proficient enough to generate complex software applications from a single prompt. As their capabilities have grown, human oversight has increasingly shifted f…
Sem-Detect: Semantic Level Detection of AI Generated Peer-Reviews
André V. Duarte, Brian Tufts, Aditya Oke +3
How can we distinguish whether a peer review was written by a human or generated by an AI model? We argue that, in this setting, authorship should not be attributed solely from the…
Leveraging LLMs to Streamline the Review of Public Funding Applications
Joao D. S. Marques, Andre V. Duarte, Andre Carvalho +3
Every year, the European Union and its member states allocate millions of euros to fund various development initiatives. However, the increasing number of applications received for…
RECAP: Reproducing Copyrighted Data from LLMs Training with an Agentic Pipeline
André V. Duarte, Xuying li, Bin Zeng +3
If we cannot inspect the training data of a large language model (LLM), how can we ever know what it has seen? We believe the most compelling evidence arises when the model itself…
DIS-CO: Discovering Copyrighted Content in VLMs Training Data
André V. Duarte, Xuandong Zhao, Arlindo L. Oliveira +1
How can we verify whether copyrighted content was used to train a large vision-language model (VLM) without direct access to its training data? Motivated by the hypothesis that a V…
LumberChunker: Long-Form Narrative Document Segmentation
André V. Duarte, João Marques, Miguel Graça +3
Modern NLP tasks increasingly rely on dense retrieval methods to access up-to-date and relevant contextual information. We are motivated by the premise that retrieval benefits from…
DE-COP: Detecting Copyrighted Content in Language Models Training Data
André V. Duarte, Xuandong Zhao, Arlindo L. Oliveira +1
How can we detect if copyrighted content was used in the training process of a language model, considering that the training data is typically undisclosed? We are motivated by the…
Improving Address Matching using Siamese Transformer Networks
André V. Duarte, Arlindo L. Oliveira
Matching addresses is a critical task for companies and post offices involved in the processing and delivery of packages. The ramifications of incorrectly delivering a package to t…