More Like this

1. Mitigating Insecure Outputs in Large Language Models (LLMs): A Practical Educational Module

   Barek, MD_Abdul; Rahman, Md_Mostafizur; Akter, Mst_Shapna; Riad, A_B_M_Kamrul_Islam; Rahman, Md_Abdur; Hossain, Shahriar; Rahman, Akond; Wu, Fan (July 2024, IEEE International Conference on Computers, Software, and Applications (COMPSAC 2024))

   Large Language Models (LLMs) have extensive ability to produce promising output. Nowadays, people are increasingly relying on them due to easy accessibility, rapid and outstanding outcomes. However, the use of these results without appropriate scrutiny poses serious security risks, particularly when they are integrated with other software, APIs, or plugins. This is because the LLM outputs are highly dependent on the prompts they receive. Therefore, it is essential to carefully clean these outputs before using them in additional software environments. This paper is designed to teach students about the potential dangers of contaminated LLM output within the context of web development through prelab, hands-on, and postlab experiences. Hands-on lab provides practical guidance on how to handle LLM vulnerabilities to make applications safe with some real-world examples in Python. This approach aims to provide students with a deeper understanding of the precautions necessary to ensure software against the vulnerabilities introduced by LLM output. 

   more » « less
2. Mitigating Insecure Outputs in Large Language Models (LLMs): A Practical Educational Module

   Barek, Md; Rahman, Md; Akter, Mst; Riad, ABM; Rahman, Md; Shahriar, Hossain; Rahman, Akond; Wu, Fan (July 2024, Proc. of The 48th IEEE International Conference on Computers, Software, and Applications (COMPSAC 2024))

   Large Language Models (LLMs) have extensive ability to produce promising output. Nowadays, people are increasingly relying on them due to easy accessibility, rapid and outstanding outcomes. However, the use of these results without appropriate scrutiny poses serious security risks, particularly when they are integrated with other software, APIs, or plugins. This is because the LLM outputs are highly dependent on the prompts they receive. Therefore, it is essential to carefully clean these outputs before using them in additional software environments. This paper is designed to teach students about the potential dangers of contaminated LLM output within the context of web development through prelab, handson, and postlab experiences. Hands-on lab provides practical guidance on how to handle LLM vulnerabilities to make applications safe with some real-world examples in Python. This approach aims to provide students with a deeper understanding of the precautions necessary to ensure software against the vulnerabilities introduced by LLM output. 

   more » « less
3. HILDE: Intentional Code Generation via Human-in-the-Loop Decoding

   Gonzalez, Emmanuel Anaya; Rothkopf, Raven; Lerner, Sorin; Polikarpova, Nadia (October 2025, 2025 IEEE Symposium on Visual Languages and Human-Centric Computing)

   While AI programming tools hold the promise of increasing programmers’ capabilities and productivity to a remarkable degree, they often exclude users from essential decision making processes, causing many to effectively “turn off their brains” and over-rely on solutions provided by these systems. These behaviors can have severe consequences in critical domains, like software security. We propose Human-in-the-Loop Decoding, a novel interaction technique that allows users to observe and directly influence LLM decisions during code generation, in order to align the model’s output with their personal requirements. We implement this technique in HILDE, a code completion assistant that highlights critical decisions made by the LLM and provides local alternatives for the user to explore. In a within-subjects study (N=18) on security-related tasks, we found that HILDE led participants to generate significantly fewer vulnerabilities and better align code generation with their goals compared to a traditional code completion assistant. 

   more » « less
4. HILDE: Intentional Code Generation via Human-in-the-Loop Decoding

   Gonzalez, Emmanuel Anaya; Rothkopf, Raven; Lerner, Sorin; Polikarpova, Nadia (October 2025, Proceedings)

   While AI programming tools hold the promise of increasing programmers’ capabilities and productivity to a remarkable degree, they often exclude users from essential decision making processes, causing many to effectively “turn off their brains” and over-rely on solutions provided by these systems. These behaviors can have severe consequences in critical domains, like software security. We propose Human-in-the-Loop Decoding, a novel interaction technique that allows users to observe and directly influence LLM decisions during code generation, in order to align the model’s output with their personal requirements. We implement this technique in HILDE, a code completion assistant that highlights critical decisions made by the LLM and provides local alternatives for the user to explore. In a within-subjects study (N=18) on security-related tasks, we found that HILDE led participants to generate significantly fewer vulnerabilities and better align code generation with their goals compared to a traditional code completion assistant. 

   more » « less
5. Measuring Reproducibility in LLM Research: A Rubric and Cross-Topic Corpus Study

   Chong, Chun_Jie; Hou, Chenxi; Liu, Sichao; Yao, Zhihao (July 2026, Association for Computing Machinery)

   Large language model (LLM) research now spans all computer research areas, yet the practical reproducibility of that literature remains uneven. Releasing code is no longer enough to ensure reproducibility because LLM-related papers may rely on gated model weights, mutable application programming interfaces (APIs), hidden system prompts, unstable dependency stacks, expensive accelerator resources, or evaluation backends that drift after publication. We present a cross-topic corpus study of LLM papers covering six categories: foundation models, alignment, retrieval-augmented generation (RAG) and agent systems, code generation, security/privacy, and evaluation/domain systems. Each paper is scored on five axes: model availability, software packaging, data accessibility, hardware approachability, and evaluation durability. The resulting scores estimate rerun feasibility from the public artifact without end-to-end reproduction. We report three findings, (1) software and benchmark artifacts are often more durable than model access: many papers remain inspectable or partially rerunnable even when the original model cannot be reused directly; (2) code generation and evaluation/domain papers are the most reproducible in our sample because they frequently pair LLM outputs with executable checks; (3) foundation-model papers remain constrained by compute, while security/privacy papers are often cheap to rerun but fragile because they depend on mutable APIs and interfaces. We provide empirical evidence about how models, software, hardware, and evaluation drift shape the practical reproducibility of LLM research. 

   more » « less