MemGuard-Alpha: Limits of Membership Inference for Detecting and Filtering Memorization-Contaminated Signals in LLM-Based Financial Forecasting
Authors: Anisha Roy, Dip Roy
Organizations: Department of Electronics and Communication Engineering, Jaypee Institute of Information Technology, Noida, India · Department of Computer Science and Engineering, Indian Institute of Technology Patna, India
Large language models are increasingly used to generate financial alpha signals, but many have memorized the historical data in their training corpora, producing apparent accuracy that collapses out of sample. Membership inference attacks (MIA) have been proposed as a diagnostic. What has not been established is whether MIA scores are informative about memorization in this setting, or whether signal-level filtering built on them helps once realistic costs are applied.We introduce MemGuard-Alpha, comprising a composite contamination score combining five MIA methods with a temporal proximity feature, and Cross-Model Memorization Disagreement, which exploits variation in training cutoffs across models. We then audit both. Across seven LLMs (124M-7B), 50 S&P 100 constituents, 42,800 prompts and 299,600 prompt-model MIA scores spanning 2019-2024, three findings emerge.First, where in-sample status is defined by a training cutoff, a temporal proximity feature recovers that label perfectly (ROC-AUC 1.000) because it is a monotone transform of the defining variable; any composite score containing such a feature reports separation that is arithmetic rather than detection. Second, the discriminative power of the MIA scores is largely attributable to scale differences between models: asked at a fixed date which models had that date in training, raw scores appear highly informative (AUC up to 0.99), but under three independent within-model normalizations discrimination falls to 0.487-0.537. Third, with transaction costs applied symmetrically, no filtering variant improves risk-adjusted performance over the unfiltered ensemble, none attains significant Fama-French five-factor alpha, and excluding the single weakest model outperforms every contamination-based filter.We report these as negative results with the failure modes that produced them, and release all artifacts needed to reproduce them.
Large Language Models (LLMs) utilize large amounts of data for their training, some of which may come from copyrighted sources. Membership Inference Attacks (MIA) aim to detect those documents and whether they have been included in the training corpora of the LLMs. The black-box MIAs require a significant amount of data manipulation; therefore, their comparison is often challenging. We study state-of-the-art (SOTA) MIAs under the black-box assumptions and compare them to each other using a unified set of datasets to determine if any of them can reliably detect membership under SOTA LLMs. In addition, a new method, called the Familiarity Ranking, was developed to showcase a possible approach to black-box MIAs, thereby giving LLMs more freedom in their expression to understand their reasoning better. The results indicate that none of the methods are capable of reliably detecting membership in LLMs, as shown by an AUC-ROC of approximately 0.5 for all methods across several LLMs. The higher TPR and FPR for more advanced LLMs indicate higher reasoning and generalizing capabilities, showcasing the difficulty of detecting membership in LLMs using black-box MIAs.
Backtesting large language models (LLMs) on historical financial data is unreliable because pre-training cuts off after the events happened. An LLM trained in 2024 already "knows" which way 2018-2020 stocks moved. We name this failure parametric look-ahead bias and propose FinCAD, an inference-time adaptation of Context-Aware Decoding that suppresses an LLM's memory of historical outcomes without retraining. FinCAD pairs an adversarial bias-discovery pipeline that learns a model-specific memory-activating prior prompt with an entity- and date-adaptive rule that scales the CAD strength to per-(entity, date) memorisation, so the penalty fires on memorised in-sample dates and decays to zero out-of-sample. Across five 7-14B LLMs and five mega-cap equities, FinCAD cuts in-sample backtest returns by up to -67.1% on memorised dates while leaving 2025 out-of-sample returns within $8K and Sharpe within 0.10 of baseline, and preserves general-purpose reasoning within 1.7 pts. On an eleven-model leaderboard, it raises the in-sample / out-of-sample Spearman correlation from +0.779 to +0.846, recovering rankings that genuinely predict out-of-sample performance.
Language models (LMs) can "memorize" information, i.e., encode training data in their weights in such a way that inference-time queries can lead to verbatim regurgitation of that data. This ability to extract training data can be problematic, for example, when data are private or sensitive. In this work, we investigate methods to mitigate memorization: three regularizer-based, three finetuning-based, and eleven machine unlearning-based methods, with five of the latter being new methods that we introduce. We also introduce TinyMem, a suite of small, computationally-efficient LMs for the rapid development and evaluation of memorization-mitigation methods. We demonstrate that the mitigation methods that we develop using TinyMem can successfully be applied to production-grade LMs, and we determine via experiment that: regularizer-based mitigation methods are slow and ineffective at curbing memorization; fine-tuning-based methods are effective at curbing memorization, but overly expensive, especially for retaining higher accuracies; and unlearning-based methods are faster and more effective, allowing for the precise localization and removal of memorized information from LM weights prior to inference. We show, in particular, that our proposed unlearning method BalancedSubnet outperforms other mitigation methods at removing memorized information while preserving performance on target tasks.
Mansi Sakarvadia, Aswathy Ajith, Arham Khan +6
University of Chicago · Argonne National Laboratory · Lawrence Berkeley National Laboratory +3