ENTERPRISE AI ANALYSIS
How Do Semantically Equivalent Code Transformations Impact Membership Inference on LLMs for Code?
The success of large language models for code (LLM4Code) relies on vast amounts of data, raising concerns about intellectual property compliance and the unauthorized use of license-restricted code. While Membership Inference (MI) techniques have been proposed to detect such usage, their effectiveness can be undermined by semantically equivalent code transformations (SECT) which modify code syntax while preserving semantics.
Executive Impact: Key Findings at a Glance
Our analysis reveals critical insights into the vulnerabilities of LLM4Code to semantic transformations and their implications for intellectual property and license compliance.
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MI Detection Reduction (Rule 1)
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Worst-case Accuracy Drop
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Models with <1% Accuracy Drop
Deep Analysis & Enterprise Applications
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Problem & Motivation
Methodology
Key Findings
Strategic Implications
The Rising Threat of Obfuscated Code
The success of large language models for code (LLM4Code) is built on vast datasets, including public open-source repositories and proprietary code. This reliance raises significant concerns about intellectual property (IP) compliance and the potential for unauthorized use of license-restricted code.
Membership Inference (MI) techniques have emerged as a potential audit mechanism to detect unauthorized data usage. However, Semantically Equivalent Code Transformations (SECT) pose a critical threat. SECT modifies code syntax while preserving its original functionality and semantics, potentially allowing malicious actors to bypass MI detection without impacting model performance.
Our central research question investigates: "How Do Semantically Equivalent Code Transformations Impact Membership Inference on LLMs for Code?"
Enterprise Process Flow
Program Transformation
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Fine-tuning
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Member Inference
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Causal Inference
Advanced MI & Causal Analysis Techniques
Our methodology systematically investigates how SECT rules impact MI detection. We first collected 23 SECT rules applicable to Java to generate semantically equivalent datasets. We then fine-tuned eight LLM4Code models, evaluating task performance and MI success rates.
For MI, we employed three distinct scoring functions: LOSS (measures average token-level negative log-likelihood), MIN_K (focuses on k% lowest-likelihood tokens), and ZLIB (calibrates loss by Zlib-compressed size). To validate our findings and move beyond correlation, we conducted a causal analysis using a Structural Causal Model (SCM), estimating Average Treatment Effects (ATEs) and performing refutation tests to ensure robustness.
-10.19%
MI Detection Reduction (LOSS) by Rule 1 on DeepSeek-Coder-1.3B
Disrupting MI: Rule 1's Dominance
Our empirical results demonstrate that models fine-tuned on semantically equivalent datasets exhibit highly consistent performance, with 135 out of 138 models experiencing no more than a 1% drop in accuracy. However, SECT significantly impacts MI performance.
Specifically, Rule 1 (RenameVariable) proved to be the most effective, reducing MI success by 10.19% on deepseek-coder-1.3b (LOSS metric), while task accuracy dropped by only 0.63%. This highlights its potent ability to obscure restricted code with minimal performance cost.
| Aspect | Rule 1 (RenameVariable) | Rule 13 (ModifyConstant) | All Rules Combined |
|---|---|---|---|
| Avg MI Reduction (LOSS) - CodeGPT | ~7.99% | ~1.06% | ~3.40% |
| Avg MI Reduction (LOSS) - DeepSeek-Coder-1.3b | ~10.19% | ~7.24% | ~2.79% |
| Avg MI Reduction (LOSS) - Starcoder2-3b | ~4.81% | ~0.56% | ~6.92% |
| Overall Effectiveness | Most Significant Impact (Consistently) | Moderate Impact (Varies) | Often Less than Best Individual |
Causal Analysis Validates Rule 1's Impact
Validated Impact of Variable Renaming
Our causal analysis confirms that Rule 1 (RenameVariable) has the strongest causal effect in disrupting MI detection. This rule directly impacts model memorization, making it significantly harder for MI techniques to identify transformed code as part of the training data.
While other transformations (e.g., Rule 7, Rule 8, and Rule 13) show moderate effects, we found that combining multiple transformations does not further reduce MI effectiveness beyond Rule 1 alone. This suggests a diminishing marginal effect, highlighting a critical loophole in current compliance auditing mechanisms.
Closing the Loophole: Redefining Compliance Audits
Our findings expose a critical loophole in license compliance enforcement for LLM4Code: MI detection can be substantially weakened by transformation-based obfuscation techniques, particularly variable renaming. This creates a significant risk that license-restricted code, once transformed, can evade current compliance checks without clear violation signals.
To address this, we advocate for customized MI techniques that account for code transformations, potentially by leveraging SECT to expand member sets or assigning lower weights to variable names in MI methods. Furthermore, causal inference offers a powerful framework for understanding and enhancing the robustness and interpretability of LLM4Code in security and privacy contexts, going beyond mere correlations.
Future work should explore more resilient architectures, such as Neurosymbolic AI, which integrates symbolic reasoning with deep learning to better understand code semantics beyond token-level patterns, and develop robust license auditing tools that are resilient to semantic-preserving transformations.
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