# Beware EviLLM: Enabling Vulnerability Injection via Large Language Models

arXiv: https://arxiv.org/abs/2610.03857 · HTML: https://mlsanity.com/paper/2610.03857/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Prompt injection and agent security, one of 12 directions in LLM safety and security.
- Builds on 30 of its 65 references that are in the map.
- First posted on arXiv 2 Oct 2026.

## Placed in

- LLM safety and security / [Prompt injection and agent security](https://mlsanity.com/safety/prompt-injection-and-agent-security/)

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

- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory](https://mlsanity.com/paper/2410.10813/) (2024)
- [WildChat: 1M ChatGPT Interaction Logs in the Wild](https://mlsanity.com/paper/2405.01470/) (2024)
- ["Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models](https://mlsanity.com/paper/2308.03825/) (2023)
- [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021)
- [Formalizing and Benchmarking Prompt Injection Attacks and Defenses](https://mlsanity.com/paper/2310.12815/) (2023)
- [Do Users Write More Insecure Code with AI Assistants?](https://mlsanity.com/paper/2211.03622/) (2022)
- [MasterKey: Automated Jailbreak Across Multiple Large Language Model Chatbots](https://mlsanity.com/paper/2307.08715/) (2023)
- [GitHub Copilot AI pair programmer: Asset or Liability?](https://mlsanity.com/paper/2206.15331/) (2022)
- [Optimization-based Prompt Injection Attack to LLM-as-a-Judge](https://mlsanity.com/paper/2403.17710/) (2024)
- [Making Them Ask and Answer: Jailbreaking Large Language Models in Few Queries via Disguise and Reconstruction](https://mlsanity.com/paper/2402.18104/) (2024)
- [Lost at C: A User Study on the Security Implications of Large Language Model Code Assistants](https://mlsanity.com/paper/2208.09727/) (2022)
- [Don't Listen To Me: Understanding and Exploring Jailbreak Prompts of Large Language Models](https://mlsanity.com/paper/2403.17336/) (2024)
- [How Secure is Code Generated by ChatGPT?](https://mlsanity.com/paper/2304.09655/) (2023)
- [Is GitHub's Copilot as Bad as Humans at Introducing Vulnerabilities in Code?](https://mlsanity.com/paper/2204.04741/) (2022)
- [We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMs](https://mlsanity.com/paper/2406.10279/) (2024)
- [Instruction Backdoor Attacks Against Customized LLMs](https://mlsanity.com/paper/2402.09179/) (2024)
- [TrojanPuzzle: Covertly Poisoning Code-Suggestion Models](https://mlsanity.com/paper/2301.02344/) (2023)
- [An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detection](https://mlsanity.com/paper/2406.06822/) (2024)
- [The Philosopher's Stone: Trojaning Plugins of Large Language Models](https://mlsanity.com/paper/2312.00374/) (2023)
- [Prompt Packer: Deceiving LLMs through Compositional Instruction with Hidden Attacks](https://mlsanity.com/paper/2310.10077/) (2023)
- [DeceptPrompt: Exploiting LLM-driven Code Generation via Adversarial Natural Language Instructions](https://mlsanity.com/paper/2312.04730/) (2023)
- [Malla: Demystifying Real-world Large Language Model Integrated Malicious Services](https://mlsanity.com/paper/2401.03315/) (2024)
- [Assessing the Security of GitHub Copilot Generated Code -- A Targeted Replication Study](https://mlsanity.com/paper/2311.11177/) (2023)
- [Measure-Observe-Remeasure: An Interactive Paradigm for Differentially-Private Exploratory Analysis](https://mlsanity.com/paper/2406.01964/) (2024)
- [Poisoned ChatGPT Finds Work for Idle Hands: Exploring Developers' Coding Practices with Insecure Suggestions from Poisoned AI Models](https://mlsanity.com/paper/2312.06227/) (2023)
- [A User-centered Security Evaluation of Copilot](https://mlsanity.com/paper/2308.06587/) (2023)
- [Attacks on Third-Party APIs of Large Language Models](https://mlsanity.com/paper/2404.16891/) (2024)
- [Trojans in Large Language Models of Code: A Critical Review through a Trigger-Based Taxonomy](https://mlsanity.com/paper/2405.02828/) (2024)
- [HEDP: A Method for Early Forecasting Software Defects based on Human Error Mechanisms](https://mlsanity.com/paper/2110.06758/) (2021)

## Abstract (arXiv)

Advances in large language models (LLMs) have enabled AI-driven code generation from natural language specifications, introducing new attack surfaces for injecting vulnerabilities into software. Prior work has studied this problem only in benign settings where vulnerabilities are introduced inadvertently, or under unconventional threat models where the LLM itself is malicious (backdooring) or the user is the attacker (jailbreaking). In this paper, we study a more realistic threat model: a third-party adversary, with capabilities comparable to existing cybercriminals, compromises the AI code generation pipeline to deliberately introduce vulnerabilities. We call this the EviLLM attack. We have implemented two instances of EviLLM, each of which only requires the underlying LLM to be accessed through a compromised account or browser, and can inject vulnerabilities from 13 CWE classes. As we show in our feasibility study, both attack vectors are already used to implement many existing cyberattacks. Our user study shows that 7 out of 8 and 10 out of 13 participants did not notice the vulnerabilities injected by the two instances of EviLLM, and 13 out of 21 participants "rarely" or "never" considered the risk of an attack like EviLLM.