arXiv (All)
open access
The Infinite Mutation Engine? Measuring Polymorphism in LLM-Generated Offensive Code
Malware authors traditionally rely on polymorphic techniques to produce variants within a malware family, complicating detection. Generative AI enables attackers to automatically synthesize structurally diverse payloads with identical behavior, raising the question of how much evasion Large Language
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- The Infinite Mutation Engine? Measuring Polymorphism in LLM-Generated Offensive Code#157297
- Weaponizing Ground Truth: Data Poisoning Attacks by Exploiting Boundary Misalignment Between Antivirus Software and Learning-Based Detectors#1105562
- HiveTraceGuard-Pro: A Compact Generative Guardrail for Prompt Injection, Jailbreaks, and Adversarial Obfuscation#627616
- A Machine Learning Framework for Detecting and Mitigating Adversarial Prompt Attacks in Large Language Models#402307
- A Large Language Model Approach to Generating Bypass Rules for Malware Evasion in Analysis Sandbox#216735
Record · ID 1387050
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive