Mind the Gap: Robustness Risks in PII Detection Systems

• URL: http://arxiv.org/abs/2609.03464v1
• Date: Thu, 03 Sep 2026 07:23:28 GMT
• Title: Mind the Gap: Robustness Risks in PII Detection Systems
• Abstract summary: We study how different PII detection paradigms fail under noisy, unstructured, and informal inputs.<n>No single architecture is uniformly reliable across PII categories.<n>Motivated by these findings, we propose a hybrid detection pipeline with a QA-driven feedback loop for iterative risk mitigation.
• Score: 6.167318681955322
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Personally Identifiable Information (PII) detection is a foundational component of data protection infrastructure where missed entities constitute direct privacy and security risks. Although modern PII systems report strong performance on standard benchmarks, we show that these evaluations mask substantial robustness failures under realistic distribution shifts encountered in deployment. Rather than comparing state-of-the-art accuracy, we study how different PII detection paradigms fail under noisy, unstructured, and informal inputs. We construct a stress test benchmark spanning seven categories of natural distribution shift and evaluate representative systems from three widely deployed architectural families: encoder-based NER (SpaCy), rule-based hybrid detection (Presidio), and generative LLM extraction (Qwen2.5-3B). All three exhibit significant degradation on out-of-distribution inputs, but with distinct and complementary failure modes. Encoder models primarily fail on unseen surface forms and boundary detection, rule-based systems fail on non-standard formats, and LLMs exhibit entity-type confusion and generation instability. These results show that aggregate benchmark scores obscure deployment-critical weaknesses and that no single architecture is uniformly reliable across PII categories. Motivated by these findings, we propose a hybrid detection pipeline with a QA-driven feedback loop for iterative risk mitigation, and release our benchmark to support OOD-aware evaluation of PII systems.

Related papers

• Towards Diverse and Comprehensive Benchmarks for Mutual Information Estimation [47.29900218955283]
  Mutual information estimation is a central problem in machine learning and statistics.<n>Existing benchmarks evaluate estimators on simplified, low-dimensional distributions, leaving their performance on complex, realistic data largely unexplored.<n>We address this gap with a comprehensive benchmarking framework grounded in a unified copula-theoretic perspective.
  arXiv  Detail & Related papers  (2026-07-03T16:55:55Z)
• Beyond Raw Detection Scores: Markov-Informed Calibration for Boosting Machine-Generated Text Detection [105.14032334647932]
  Machine-generated texts (MGTs) pose risks such as disinformation and phishing, highlighting the need for reliable detection.<n> Metric-based methods, which extract statistically distinguishable features of MGTs, are often more practical than complex model-based methods that are prone to overfitting.<n>We propose a Markov-informed score calibration strategy that models two relationships of context detection scores that may aid calibration.
  arXiv  Detail & Related papers  (2026-02-08T16:06:12Z)
• Refining Decision Boundaries In Anomaly Detection Using Similarity Search Within the Feature Space [3.3202103799131795]
  We introduce SDA2E, a Sparse Dual Adversarial Attention-based AutoEncoder designed to learn compact and discriminative latent representations from imbalanced, high-dimensional data.<n>We propose a similarity-guided active learning framework that integrates three novel strategies to refine decision boundaries efficiently.<n>We evaluate SDA2E extensively across 52 imbalanced datasets, including multiple DARPA Transparent Computing scenarios, and benchmark it against 15 state-of-the-art anomaly detection methods.
  arXiv  Detail & Related papers  (2026-02-02T23:55:08Z)
• I-GLIDE: Input Groups for Latent Health Indicators in Degradation Estimation [1.034052616244602]
  This paper introduces a novel framework for health indicators (HIs) construction, advancing three key contributions.<n>We adapt Reconstruction along Projected Pathways (RaPP) as a health indicator (HI) for RUL prediction for the first time, showing that it outperforms traditional reconstruction error metrics.<n>We also propose indicator groups, a paradigm that isolates sensor subsets to model system-specific degradations, giving rise to our novel method, I-GLIDE.
  arXiv  Detail & Related papers  (2025-11-26T09:39:35Z)
• Benchmarking Corruption Robustness of LVLMs: A Discriminative Benchmark and Robustness Alignment Metric [49.393713730706445]
  We introduce Bench-C, a benchmark emphasizing discriminative samples for assessing corruption robustness.<n>We propose the Robustness Alignment Score (RAS), a unified metric that measures degradation in logit-level prediction structure.
  arXiv  Detail & Related papers  (2025-11-24T12:07:56Z)
• Continual Action Quality Assessment via Adaptive Manifold-Aligned Graph Regularization [53.82400605816587]
  Action Quality Assessment (AQA) quantifies human actions in videos, supporting applications in sports scoring, rehabilitation, and skill evaluation.<n>A major challenge lies in the non-stationary nature of quality distributions in real-world scenarios.<n>We introduce Continual AQA (CAQA), which equips with Continual Learning capabilities to handle evolving distributions.
  arXiv  Detail & Related papers  (2025-10-08T10:09:47Z)
• Rethinking Evaluation of Infrared Small Target Detection [105.59753496831739]
  This paper introduces a hybrid-level metric incorporating pixel- and target-level performance, proposing a systematic error analysis method, and emphasizing the importance of cross-dataset evaluation.<n>An open-source toolkit has be released to facilitate standardized benchmarking.
  arXiv  Detail & Related papers  (2025-09-21T02:45:07Z)
• Fair Deepfake Detectors Can Generalize [51.21167546843708]
  We show that controlling for confounders (data distribution and model capacity) enables improved generalization via fairness interventions.<n>Motivated by this insight, we propose Demographic Attribute-insensitive Intervention Detection (DAID), a plug-and-play framework composed of: i) Demographic-aware data rebalancing, which employs inverse-propensity weighting and subgroup-wise feature normalization to neutralize distributional biases; and ii) Demographic-agnostic feature aggregation, which uses a novel alignment loss to suppress sensitive-attribute signals.<n>DAID consistently achieves superior performance in both fairness and generalization compared to several state-of-the-art
  arXiv  Detail & Related papers  (2025-07-03T14:10:02Z)
• TrustLoRA: Low-Rank Adaptation for Failure Detection under Out-of-distribution Data [62.22804234013273]
  We propose a simple failure detection framework to unify and facilitate classification with rejection under both covariate and semantic shifts.<n>Our key insight is that by separating and consolidating failure-specific reliability knowledge with low-rank adapters, we can enhance the failure detection ability effectively and flexibly.
  arXiv  Detail & Related papers  (2025-04-20T09:20:55Z)
• Prior2Former -- Evidential Modeling of Mask Transformers for Assumption-Free Open-World Panoptic Segmentation [74.55677741919035]
  We propose Prior2Former (P2F), the first approach for segmentation vision transformers rooted in evidential learning.<n>P2F extends the mask vision transformer architecture by incorporating a Beta prior for computing model uncertainty in pixel-wise binary mask assignments.<n>Unlike most segmentation models addressing unknown classes, P2F operates without access to OOD data samples or contrastive training on void (i.e., unlabeled) classes.
  arXiv  Detail & Related papers  (2025-04-07T08:53:14Z)
• BELLS: A Framework Towards Future Proof Benchmarks for the Evaluation of LLM Safeguards [43.86118338226387]
  We introduce the Benchmarks for the Evaluation of LLM Safeguards (BELLS) BELLS is a structured collection of tests, organized into three categories: established failure tests, emerging failure tests and next-gen architecture tests. We implement and share the first next-gen architecture test, using the MACHIAVELLI environment, along with an interactive visualization of the dataset.
  arXiv  Detail & Related papers  (2024-06-03T14:32:30Z)
• Fault Detection and Monitoring using a Data-Driven Information-Based Strategy: Method, Theory, and Application [5.056456697289351]
  We propose an information-driven fault detection method based on a novel concept drift detector.<n>The method is tailored to identifying drifts in input-output relationships of additive noise models.<n>We prove several theoretical properties of the proposed MI-based fault detection scheme.
  arXiv  Detail & Related papers  (2024-05-06T17:43:39Z)
• Exploring the Relationship between Samples and Masks for Robust Defect Localization [1.90365714903665]
  This paper proposes a one-stage framework that detects defective patterns directly without the modeling process. Explicit information that could indicate the position of defects is intentionally excluded to avoid learning any direct mapping. Results show that the proposed method is 2.9% higher than the SOTA methods in F1-Score, while substantially outperforming SOTA methods in generalizability.
  arXiv  Detail & Related papers  (2023-06-19T06:41:19Z)
• Free Lunch for Generating Effective Outlier Supervision [46.37464572099351]
  We propose an ultra-effective method to generate near-realistic outlier supervision. Our proposed textttBayesAug significantly reduces the false positive rate over 12.50% compared with the previous schemes.
  arXiv  Detail & Related papers  (2023-01-17T01:46:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.