SoK: Software Compartmentalization

• URL: http://arxiv.org/abs/2410.08434v1
• Date: Fri, 11 Oct 2024 00:38:45 GMT
• Title: SoK: Software Compartmentalization
• Authors: Hugo Lefeuvre, Nathan Dautenhahn, David Chisnall, Pierre Olivier,
• Abstract summary: Decomposing large systems into smaller components has long been recognized as an effective means to minimize the impact of exploits. Despite historical roots, demonstrated benefits, and a plethora of research efforts in academia and industry, the compartmentalization of software is still not a mainstream practice. We propose a unified model for the systematic analysis, comparison, and directing of compartmentalization approaches.
• Score: 3.058923790501231
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Decomposing large systems into smaller components with limited privileges has long been recognized as an effective means to minimize the impact of exploits. Despite historical roots, demonstrated benefits, and a plethora of research efforts in academia and industry, the compartmentalization of software is still not a mainstream practice. This paper investigates why, and how this status quo can be improved. Noting that existing approaches are fraught with inconsistencies in terminology and analytical methods, we propose a unified model for the systematic analysis, comparison, and directing of compartmentalization approaches. We use this model to review 211 research efforts and analyze 61 mainstream compartmentalized systems, confronting them to understand the limitations of both research and production works. Among others, our findings reveal that mainstream efforts largely rely on manual methods, custom abstractions, and legacy mechanisms, poles apart from recent research. We conclude with recommendations: compartmentalization should be solved holistically; progress is needed towards simplifying the definition of compartmentalization policies; towards better challenging our threat models in the light of confused deputies and hardware limitations; as well as towards bridging the gaps we pinpoint between research and mainstream needs. This paper not only maps the historical and current landscape of compartmentalization, but also sets forth a framework to foster their evolution and adoption.

Related papers

• Decoding the Multimodal Maze: A Systematic Review on the Adoption of Explainability in Multimodal Attention-based Models [0.0]
  This systematic literature review analyzes research published between January 2020 and early 2024 that focuses on the explainability of multimodal models.<n>We find that evaluation methods for XAI in multimodal settings are largely non-systematic, lacking consistency, robustness, and consideration for modality-specific cognitive and contextual factors.
  arXiv  Detail & Related papers  (2025-08-06T13:14:20Z)
• A Replica for our Democracies? On Using Digital Twins to Enhance Deliberative Democracy [0.41942958779358663]
  This paper explores Digital Twin (DT) technology as a computational testing ground for deliberative systems.<n>By constructing dynamic models that simulate real-world deliberation, DTs allow researchers and policymakers to rigorously test "what-if" scenarios.
  arXiv  Detail & Related papers  (2025-04-07T23:14:41Z)
• A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond [88.5807076505261]
  Large Reasoning Models (LRMs) have demonstrated strong performance gains by scaling up the length of Chain-of-Thought (CoT) reasoning during inference. A growing concern lies in their tendency to produce excessively long reasoning traces. This inefficiency introduces significant challenges for training, inference, and real-world deployment.
  arXiv  Detail & Related papers  (2025-03-27T15:36:30Z)
• Speculative Decoding and Beyond: An In-Depth Survey of Techniques [4.165029665035158]
  Sequential dependencies present a fundamental bottleneck in deploying large-scale autoregressive models. Recent advances in generation-refinement frameworks demonstrate that this trade-off can be significantly mitigated.
  arXiv  Detail & Related papers  (2025-02-27T03:53:45Z)
• Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More Measurable Objectives [52.863024096759816]
  Misaligned research objectives have hindered progress in adversarial robustness research over the past decade. We argue that realigned objectives are necessary for meaningful progress in adversarial alignment.
  arXiv  Detail & Related papers  (2025-02-17T15:28:40Z)
• Case for a unified surrogate modelling framework in the age of AI [0.0]
  We argue for the urgent need for a unified framework to guide the development and evaluation of surrogate models. This paper seeks to improve the reliability of surrogate modelling, foster cross-disciplinary knowledge transfer, and, as a result, accelerate scientific progress.
  arXiv  Detail & Related papers  (2025-02-10T18:31:15Z)
• Causality can systematically address the monsters under the bench(marks) [64.36592889550431]
  Benchmarks are plagued by various biases, artifacts, or leakage. Models may behave unreliably due to poorly explored failure modes. causality offers an ideal framework to systematically address these challenges.
  arXiv  Detail & Related papers  (2025-02-07T17:01:37Z)
• Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems [92.89673285398521]
  o1-like reasoning systems have demonstrated remarkable capabilities in solving complex reasoning tasks. We introduce an imitate, explore, and self-improve'' framework to train the reasoning model. Our approach achieves competitive performance compared to industry-level reasoning systems.
  arXiv  Detail & Related papers  (2024-12-12T16:20:36Z)
• Navigating Shortcuts, Spurious Correlations, and Confounders: From Origins via Detection to Mitigation [21.21130450731374]
  Clever Hans behavior, spurious correlations, or confounders, present a significant challenge in machine learning and AI. Research in this area remains fragmented across various terminologies, hindering the progress of the field as a whole. We introduce a unifying taxonomy by providing a formal definition of shortcuts and bridging the diverse terms used in the literature.
  arXiv  Detail & Related papers  (2024-12-06T16:10:13Z)
• Towards a Unified View of Preference Learning for Large Language Models: A Survey [88.66719962576005]
  Large Language Models (LLMs) exhibit remarkably powerful capabilities. One of the crucial factors to achieve success is aligning the LLM's output with human preferences. We decompose all the strategies in preference learning into four components: model, data, feedback, and algorithm.
  arXiv  Detail & Related papers  (2024-09-04T15:11:55Z)
• Resilience of Deep Learning applications: a systematic literature review of analysis and hardening techniques [3.265458968159693]
  The review is based on 220 scientific articles published between January 2019 and March 2024. The authors adopt a classifying framework to interpret and highlight research similarities and peculiarities.
  arXiv  Detail & Related papers  (2023-09-27T19:22:19Z)
• Endogenous Macrodynamics in Algorithmic Recourse [52.87956177581998]
  Existing work on Counterfactual Explanations (CE) and Algorithmic Recourse (AR) has largely focused on single individuals in a static environment. We show that many of the existing methodologies can be collectively described by a generalized framework. We then argue that the existing framework does not account for a hidden external cost of recourse, that only reveals itself when studying the endogenous dynamics of recourse at the group level.
  arXiv  Detail & Related papers  (2023-08-16T07:36:58Z)
• REX: Rapid Exploration and eXploitation for AI Agents [103.68453326880456]
  We propose an enhanced approach for Rapid Exploration and eXploitation for AI Agents called REX. REX introduces an additional layer of rewards and integrates concepts similar to Upper Confidence Bound (UCB) scores, leading to more robust and efficient AI agent performance.
  arXiv  Detail & Related papers  (2023-07-18T04:26:33Z)
• GLUECons: A Generic Benchmark for Learning Under Constraints [102.78051169725455]
  In this work, we create a benchmark that is a collection of nine tasks in the domains of natural language processing and computer vision. We model external knowledge as constraints, specify the sources of the constraints for each task, and implement various models that use these constraints.
  arXiv  Detail & Related papers  (2023-02-16T16:45:36Z)
• Deep Intrinsically Motivated Exploration in Continuous Control [0.0]
  In continuous systems, exploration is often performed through undirected strategies in which parameters of the networks or selected actions are perturbed by random noise. We adapt existing theories on animal motivational systems into the reinforcement learning paradigm and introduce a novel directed exploration strategy. Our framework extends to larger and more diverse state spaces, dramatically improves the baselines, and outperforms the undirected strategies significantly.
  arXiv  Detail & Related papers  (2022-10-01T14:52:16Z)
• f-Domain-Adversarial Learning: Theory and Algorithms [82.97698406515667]
  Unsupervised domain adaptation is used in many machine learning applications where, during training, a model has access to unlabeled data in the target domain. We derive a novel generalization bound for domain adaptation that exploits a new measure of discrepancy between distributions based on a variational characterization of f-divergences.
  arXiv  Detail & Related papers  (2021-06-21T18:21:09Z)
• Individual Explanations in Machine Learning Models: A Survey for Practitioners [69.02688684221265]
  The use of sophisticated statistical models that influence decisions in domains of high societal relevance is on the rise. Many governments, institutions, and companies are reluctant to their adoption as their output is often difficult to explain in human-interpretable ways. Recently, the academic literature has proposed a substantial amount of methods for providing interpretable explanations to machine learning models.
  arXiv  Detail & Related papers  (2021-04-09T01:46:34Z)
• Metrics and continuity in reinforcement learning [34.10996560464196]
  We introduce a unified formalism for defining topologies through the lens of metrics. We establish a hierarchy amongst these metrics and demonstrate their theoretical implications on the Markov Decision Process. We complement our theoretical results with empirical evaluations showcasing the differences between the metrics considered.
  arXiv  Detail & Related papers  (2021-02-02T14:30:41Z)
• Unsupervised Domain Adaptation in Semantic Segmentation: a Review [22.366638308792734]
  The aim of this paper is to give an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation.
  arXiv  Detail & Related papers  (2020-05-21T20:10:38Z)

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.