Papers by Mukur Gupta
Sense and Sensitivity: Examining the Influence of Semantic Recall on Long Context Code Understanding (2026.acl-long)
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| Challenge: | Large language models are increasingly used for understanding large codebases, but whether they understand operational semantics of long code context is unclear. |
| Approach: | They propose a task that achieves high semantic recall sensitivity through unpredictable operations. |
| Outcome: | The proposed task SemTrace achieves high semantic recall sensitivity through unpredictable operations. |
XOXO: Stealthy Cross-Origin Context Poisoning Attacks against AI Coding Assistants (2026.acl-long)
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| Challenge: | Cross-Origin Context Poisoning (XOXO) exploits this automatic context inclusion by subtly manipulating code without changing its semantics. |
| Approach: | They propose a novel attack that exploits automatic context inclusion by subtly manipulating code without changing its semantics. |
| Outcome: | The proposed attack achieves 73.20% success rates against eight state-of-the-art models including GPT 4.1 and Claude 3.5 Sonnet v2 and vulnerability injection rates up to 66.67%. |
CodeSCM: Causal Analysis for Multi-Modal Code Generation (2025.naacl-long)
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| Challenge: | Prior work has shown that multimodal prompts can be highly sensitive, where small adjustments might result in drastically different responses from the model. |
| Approach: | They propose a Structural Causal Model (SCM) for analyzing multi-modal code generation using large language models (LLMs). |
| Outcome: | The proposed model is based on the principles of Causal Mediation Analysis and quantifies the causal effects of different prompt modalities on the model. |