# Enhancing AI-based Generation of Software Exploits with Contextual Information

arXiv: https://arxiv.org/abs/2408.02402 · HTML: https://mlsanity.com/paper/2408.02402/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 64 references that are in the map.
- First posted on arXiv 5 Aug 2024.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [LIMA: Less Is More for Alignment](https://mlsanity.com/paper/2305.11206/) (2023)
- [CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis](https://mlsanity.com/paper/2203.13474/) (2022)
- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [Unified Pre-training for Program Understanding and Generation](https://mlsanity.com/paper/2103.06333/) (2021)
- [CodeT5+: Open Code Large Language Models for Code Understanding and Generation](https://mlsanity.com/paper/2305.07922/) (2023)
- [From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy](https://mlsanity.com/paper/2307.00691/) (2023)
- [Context-Aware Neural Machine Translation Learns Anaphora Resolution](https://mlsanity.com/paper/1805.10163/) (2018)
- [Neural Machine Translation with Extended Context](https://mlsanity.com/paper/1708.05943/) (2017)
- [Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks](https://mlsanity.com/paper/2102.02017/) (2021)
- [On the Robustness of Code Generation Techniques: An Empirical Study on GitHub Copilot](https://mlsanity.com/paper/2302.00438/) (2023)
- [Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair](https://mlsanity.com/paper/2309.00608/) (2023)
- [On Learning Meaningful Code Changes via Neural Machine Translation](https://mlsanity.com/paper/1901.09102/) (2019)
- [Exploiting Cross-Sentence Context for Neural Machine Translation](https://mlsanity.com/paper/1704.04347/) (2017)
- [Applying CodeBERT for Automated Program Repair of Java Simple Bugs](https://mlsanity.com/paper/2103.11626/) (2021)
- [The Threat of Offensive AI to Organizations](https://mlsanity.com/paper/2106.15764/) (2021)
- [Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation](https://mlsanity.com/paper/2005.03393/) (2020)
- [StructCoder: Structure-Aware Transformer for Code Generation](https://mlsanity.com/paper/2206.05239/) (2022)
- [Vulnerabilities in AI Code Generators: Exploring Targeted Data Poisoning Attacks](https://mlsanity.com/paper/2308.04451/) (2023)
- [Shellcode_IA32: A Dataset for Automatic Shellcode Generation](https://mlsanity.com/paper/2104.13100/) (2021)
- [EVIL: Exploiting Software via Natural Language](https://mlsanity.com/paper/2109.00279/) (2021)
- [Who Evaluates the Evaluators? On Automatic Metrics for Assessing AI-based Offensive Code Generators](https://mlsanity.com/paper/2212.06008/) (2022)
- [Contrastive Learning for Context-aware Neural Machine TranslationUsing Coreference Information](https://mlsanity.com/paper/2109.05712/) (2021)
- [DualSC: Automatic Generation and Summarization of Shellcode via Transformer and Dual Learning](https://mlsanity.com/paper/2202.09785/) (2022)
- [Automating the Correctness Assessment of AI-generated Code for Security Contexts](https://mlsanity.com/paper/2310.18834/) (2023)
- [Enhancing Robustness of AI Offensive Code Generators via Data Augmentation](https://mlsanity.com/paper/2306.05079/) (2023)
- [The Power of Words: Generating PowerShell Attacks from Natural Language](https://mlsanity.com/paper/2404.12893/) (2024)

## Built on by (most cited first, 3 of 3)

- [Military AI Cyber Agents (MAICAs) Constitute a Global Threat to Critical Infrastructure](https://mlsanity.com/paper/2506.12094/) (2025)
- [Willing but Unable: Separating Refusal from Capability in Code LLMs via Abliteration](https://mlsanity.com/paper/2606.05396/) (2026)
- [Data-Centric Benchmarking of Exploit Generation in LLMs: Understanding the Impact of Fine-Tuning](https://mlsanity.com/paper/2606.15123/) (2026)

## Abstract (arXiv)

This practical experience report explores Neural Machine Translation (NMT) models' capability to generate offensive security code from natural language (NL) descriptions, highlighting the significance of contextual understanding and its impact on model performance. Our study employs a dataset comprising real shellcodes to evaluate the models across various scenarios, including missing information, necessary context, and unnecessary context. The experiments are designed to assess the models' resilience against incomplete descriptions, their proficiency in leveraging context for enhanced accuracy, and their ability to discern irrelevant information. The findings reveal that the introduction of contextual data significantly improves performance. However, the benefits of additional context diminish beyond a certain point, indicating an optimal level of contextual information for model training. Moreover, the models demonstrate an ability to filter out unnecessary context, maintaining high levels of accuracy in the generation of offensive security code. This study paves the way for future research on optimizing context use in AI-driven code generation, particularly for applications requiring a high degree of technical precision such as the generation of offensive code.