# Security for Machine Learning-based Software Systems: a survey of threats, practices and challenges

arXiv: https://arxiv.org/abs/2201.04736 · HTML: https://mlsanity.com/paper/2201.04736/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2022.

## Built on by (most cited first, 9 of 9)

- [Requirements Engineering for Machine Learning: A Review and Reflection](https://mlsanity.com/paper/2210.00859/) (2022)
- [A Survey on Data Quality Dimensions and Tools for Machine Learning](https://mlsanity.com/paper/2406.19614/) (2024)
- [Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security](https://mlsanity.com/paper/2410.01016/) (2024)
- [Securing the AI Supply Chain: What Can We Learn From Developer-Reported Security Issues and Solutions of AI Projects?](https://mlsanity.com/paper/2512.23385/) (2025)
- [Trustworthy AI Posture (TAIP): A Framework for Continuous AI Assurance of Agentic Systems at Horizontal and Vertical scale](https://mlsanity.com/paper/2603.03340/) (2026)
- [Threats, Vulnerabilities, and Controls of Machine Learning Based Systems: A Survey and Taxonomy](https://mlsanity.com/paper/2301.07474/) (2023)
- [On Security Weaknesses and Vulnerabilities in Deep Learning Systems](https://mlsanity.com/paper/2406.08688/) (2024)
- [ShadowLogic: Backdoors in Any Whitebox LLM](https://mlsanity.com/paper/2511.00664/) (2025)
- [Serverless AI Security: Attack Surface Analysis and Runtime Protection Mechanisms for FaaS-Based Machine Learning](https://mlsanity.com/paper/2601.11664/) (2026)

## Abstract (arXiv)

The rapid development of Machine Learning (ML) has demonstrated superior performance in many areas, such as computer vision, video and speech recognition. It has now been increasingly leveraged in software systems to automate the core tasks. However, how to securely develop the machine learning-based modern software systems (MLBSS) remains a big challenge, for which the insufficient consideration will largely limit its application in safety-critical domains. One concern is that the present MLBSS development tends to be rush, and the latent vulnerabilities and privacy issues exposed to external users and attackers will be largely neglected and hard to be identified. Additionally, machine learning-based software systems exhibit different liabilities towards novel vulnerabilities at different development stages from requirement analysis to system maintenance, due to its inherent limitations from the model and data and the external adversary capabilities. The successful generation of such intelligent systems will thus solicit dedicated efforts jointly from different research areas, i.e., software engineering, system security and machine learning. Most of the recent works regarding the security issues for ML have a strong focus on the data and models, which has brought adversarial attacks into consideration. In this work, we consider that security for machine learning-based software systems may arise from inherent system defects or external adversarial attacks, and the secure development practices should be taken throughout the whole lifecycle. While machine learning has become a new threat domain for existing software engineering practices, there is no such review work covering the topic. Overall, we present a holistic review regarding the security for MLBSS, which covers a systematic understanding from a structure review of three distinct aspects in terms of security threats...