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The prevalence and strong capability of large language models (LLMs) present significant safety and ethical risks if exploited by malicious users. To prevent the potentially deceptive usage of LLMs, recent work has proposed algorithms to detect LLM-generated text and protect LLMs. In this paper, we investigate the robustness and reliability of these LLM detectors under adversarial attacks. We study two types of attack strategies: 1) replacing certain words in an LLM’s output with their synonyms given the context; 2) automatically searching for an instructional prompt to alter the writing style of the generation. In both strategies, we leverage an auxiliary LLM to generate the word replacements or the instructional prompt. Different from previous works, we consider a challenging setting where the auxiliary LLM can also be protected by a detector. Experiments reveal that our attacks effectively compromise the performance of all detectors in the study with plausible generations, underscoring the urgent need to improve the robustness of LLM-generated text detection systems. Code is available at https://github.com/shizhouxing/LLM-Detector-Robustness
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This content will become publicly available on February 12, 2026
Evaluating GPT for use in K-12 Block Based CS Instruction Using a Transpiler and Prompt Engineering
Though the increased availability of Large Language Models (LLMs) presents signi!cant potential for change in the way students learn to program, the text-based nature of the available tools currently preclude block-based languages from much of that innovation. In an attempt to remedy this, we identify the strengths and weaknesses of using a transpiler to leverage the existing learning in commercially available LLMs and Scratch, a visual block-based programming language. Using only prompt engineering, we evaluate an LLM’s performance on two common classroom tasks in a Scratch curriculum. We evaluate the LLM’s ability to: 1) Create project solutions that compile and satisfy project requirements and 2) Analyze student projects’ completion of project requirements using natural language. In both cases, we !nd results indicating that prompt-engineering alone is insu"cient to reliably produce high-quality results. For projects of medium complexity, the LLM-generated solutions con- sistently failed to follow correct syntax or, in the few instances with correct syntax, produce correct solutions. When used for auto- grading, we found a correlation between scores assigned by the official Scratch Encore autograder and those generated by the LLM, nevertheless the discrepancies between the ‘real’ scores and the scores assigned by the LLM remained too great for the tool to be reliable in a classroom setting.
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- Award ID(s):
- 2201313
- PAR ID:
- 10585802
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400705311
- Page Range / eLocation ID:
- 388 to 394
- Subject(s) / Keyword(s):
- K-12 Block Based Programming Large Language Models Generative AI
- Format(s):
- Medium: X
- Location:
- Pittsburgh PA USA
- Sponsoring Org:
- National Science Foundation
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