# Cybersecurity is the True Frontier for Generative AI Success or Failure

arXiv: https://arxiv.org/abs/2606.28929 · HTML: https://mlsanity.com/paper/2606.28929/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 51 of its 109 references that are in the map.
- First posted on arXiv 27 Jun 2026.

## Builds on (most cited first, 30 of 51)

- [Rethinking Attention with Performers](https://mlsanity.com/paper/2009.14794/) (2020)
- [Linformer: Self-Attention with Linear Complexity](https://mlsanity.com/paper/2006.04768/) (2020)
- [Tree of Attacks: Jailbreaking Black-Box LLMs Automatically](https://mlsanity.com/paper/2312.02119/) (2023)
- [PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://mlsanity.com/paper/2109.05093/) (2021)
- [Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning](https://mlsanity.com/paper/2305.13971/) (2023)
- [Malware Detection by Eating a Whole EXE](https://mlsanity.com/paper/1710.09435/) (2017)
- [TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time](https://mlsanity.com/paper/1807.07838/) (2018)
- [SynCode: LLM Generation with Grammar Augmentation](https://mlsanity.com/paper/2403.01632/) (2024)
- [EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models](https://mlsanity.com/paper/2403.12171/) (2024)
- [SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection](https://mlsanity.com/paper/2012.07634/) (2020)
- [Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Features](https://mlsanity.com/paper/1508.03096/) (2015)
- [Automated Red Teaming with GOAT: the Generative Offensive Agent Tester](https://mlsanity.com/paper/2410.01606/) (2024)
- [Misplaced Trust: Measuring the Interference of Machine Learning in Human Decision-Making](https://mlsanity.com/paper/2005.10960/) (2020)
- [Classifying Sequences of Extreme Length with Constant Memory Applied to Malware Detection](https://mlsanity.com/paper/2012.09390/) (2020)
- [An Observational Investigation of Reverse Engineers' Processes](https://mlsanity.com/paper/1912.00317/) (2019)
- [XDA: Accurate, Robust Disassembly with Transfer Learning](https://mlsanity.com/paper/2010.00770/) (2020)
- [Frontier AI's Impact on the Cybersecurity Landscape](https://mlsanity.com/paper/2504.05408/) (2025)
- [EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers](https://mlsanity.com/paper/2506.05074/) (2025)
- [Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternatives](https://mlsanity.com/paper/1811.05372/) (2018)
- [Bytes Are All You Need: Transformers Operating Directly On File Bytes](https://mlsanity.com/paper/2306.00238/) (2023)
- [Automatic Yara Rule Generation Using Biclustering](https://mlsanity.com/paper/2009.03779/) (2020)
- [Magma: A Ground-Truth Fuzzing Benchmark](https://mlsanity.com/paper/2009.01120/) (2020)
- [A Framework for Cluster and Classifier Evaluation in the Absence of Reference Labels](https://mlsanity.com/paper/2109.11126/) (2021)
- [SoK: The Impact of Unlabelled Data in Cyberthreat Detection](https://mlsanity.com/paper/2205.08944/) (2022)
- [ELFuzz: Efficient Input Generation via LLM-driven Synthesis Over Fuzzer Space](https://mlsanity.com/paper/2506.10323/) (2025)
- [Illuminati: Towards Explaining Graph Neural Networks for Cybersecurity Analysis](https://mlsanity.com/paper/2303.14836/) (2023)
- [Recasting Self-Attention with Holographic Reduced Representations](https://mlsanity.com/paper/2305.19534/) (2023)
- [Assemblage: Automatic Binary Dataset Construction for Machine Learning](https://mlsanity.com/paper/2405.03991/) (2024)
- [Leveraging Uncertainty for Improved Static Malware Detection Under Extreme False Positive Constraints](https://mlsanity.com/paper/2108.04081/) (2021)
- [ClarAVy: A Tool for Scalable and Accurate Malware Family Labeling](https://mlsanity.com/paper/2502.02759/) (2025)

## Built on by (most cited first, 2 of 2)

- [Identifying Security Platform Product Abuse with Machine Learning](https://mlsanity.com/paper/2609.21303/) (2026)
- [An Introduction to Compression-Based Machine Learning](https://mlsanity.com/paper/2609.21309/) (2026)

## Abstract (arXiv)

Cybersecurity is a real-life test-bed for many machine learning problems at once, especially when considering modern strides in using Large Language Models (LLMs) to automate processes as ``agents.'' Cybersecurity workflows require orchestrating hundreds of standard and bespoke tools through various formats. The scale of cybersecurity data is enormous; for example, a single malware sample can be viewed as a sequence of billions of tokens. The cost of labeling any file by experts is enormous and labor-intensive, in part because an adversary (possibly a well-funded nation state actor) is attempting to subvert your detection methods. Even skilled experts may disagree on the correct label, creating ambiguity in what constitutes ground truth. When deployed, models must run quickly on billions of items a day, where low-latency is critical for operational success, in a continuously changing environment. In addition, explainability is not optional: analysts demand clear reasoning for model decisions to cope with the large number of false-positive alerts they face daily, and to quickly develop remediation and understand how something went wrong. In short, the amount of complexity cybersecurity is greater than that of natural language and computer vision, and thus we posit that cybersecurity is the better test-case for general AI progress than other, well-studied fields.