# On the Trade-Off Between Transparency and Security in Adversarial Machine Learning

arXiv: https://arxiv.org/abs/2511.11842 · HTML: https://mlsanity.com/paper/2511.11842/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 49 references that are in the map.
- First posted on arXiv 14 Nov 2025.

## Builds on (most cited first, 23 of 23)

- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks](https://mlsanity.com/paper/2003.01690/) (2020)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks](https://mlsanity.com/paper/1702.01135/) (2017)
- [Square Attack: a query-efficient black-box adversarial attack via random search](https://mlsanity.com/paper/1912.00049/) (2019)
- [RobustBench: a standardized adversarial robustness benchmark](https://mlsanity.com/paper/2010.09670/) (2020)
- [Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks](https://mlsanity.com/paper/1704.01155/) (2017)
- [Enhancing the Transferability of Adversarial Attacks through Variance Tuning](https://mlsanity.com/paper/2103.15571/) (2021)
- [Admix: Enhancing the Transferability of Adversarial Attacks](https://mlsanity.com/paper/2102.00436/) (2021)
- [Trading Inference-Time Compute for Adversarial Robustness](https://mlsanity.com/paper/2501.18841/) (2025)
- [Boosting Adversarial Transferability by Achieving Flat Local Maxima](https://mlsanity.com/paper/2306.05225/) (2023)
- [Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains](https://mlsanity.com/paper/2201.11528/) (2022)
- [LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity](https://mlsanity.com/paper/2207.13129/) (2022)
- [Adversarial Example Games](https://mlsanity.com/paper/2007.00720/) (2020)
- [Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability](https://mlsanity.com/paper/2307.07873/) (2023)
- [A Game-Theoretic Taxonomy and Survey of Defensive Deception for Cybersecurity and Privacy](https://mlsanity.com/paper/1712.05441/) (2017)
- [Cascade Adversarial Machine Learning Regularized with a Unified Embedding](https://mlsanity.com/paper/1708.02582/) (2017)
- [A Survey of Game Theoretic Approaches for Adversarial Machine Learning in Cybersecurity Tasks](https://mlsanity.com/paper/1912.02258/) (2019)
- [Advocating for Multiple Defense Strategies against Adversarial Examples](https://mlsanity.com/paper/2012.02632/) (2020)
- [Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning](https://mlsanity.com/paper/2211.14669/) (2022)
- [Towards Evaluating Transfer-based Attacks Systematically, Practically, and Fairly](https://mlsanity.com/paper/2311.01323/) (2023)
- [A Survey of Decision Making in Adversarial Games](https://mlsanity.com/paper/2207.07971/) (2022)
- [RobustBlack: Challenging Black-Box Adversarial Attacks on State-of-the-Art Defenses](https://mlsanity.com/paper/2412.20987/) (2024)

## Abstract (arXiv)

Transparency and security are both central to Responsible AI, but they may conflict in adversarial settings. We investigate the strategic effect of transparency for agents through the lens of transferable adversarial example attacks. In transferable adversarial example attacks, attackers maliciously perturb their inputs using surrogate models to fool a defender's target model. These models can be defended or undefended, with both players having to decide which to use. Using a large-scale empirical evaluation of nine attacks across 181 models, we find that attackers are more successful when they match the defender's decision; hence, obscurity could be beneficial to the defender. With game theory, we analyze this trade-off between transparency and security by modeling this problem as both a Nash game and a Stackelberg game, and comparing the expected outcomes. Our analysis confirms that only knowing whether a defender's model is defended or not can sometimes be enough to damage its security. This result serves as an indicator of the general trade-off between transparency and security, suggesting that transparency in AI systems can be at odds with security. Beyond adversarial machine learning, our work illustrates how game-theoretic reasoning can uncover conflicts between transparency and security.