mlsanityData to 14 Oct 2026

Demystifying RCE Vulnerabilities in LLM-Integrated Apps

arXiv 2309.02926 ↗First posted 6 Sep 2023cs.CRcited by 37

Not on the map. Why?

Abstract (arXiv)

LLMs show promise in transforming software development, with a growing interest in integrating them into more intelligent apps. Frameworks like LangChain aid LLM-integrated app development, offering code execution utility/APIs for custom actions. However, these capabilities theoretically introduce Remote Code Execution (RCE) vulnerabilities, enabling remote code execution through prompt injections. No prior research systematically investigates these frameworks' RCE vulnerabilities or their impact on applications and exploitation consequences. Therefore, there is a huge research gap in this field. In this study, we propose LLMSmith to detect, validate and exploit the RCE vulnerabilities in LLM-integrated frameworks and apps. To achieve this goal, we develop two novel techniques, including 1) a lightweight static analysis to examine LLM integration mechanisms, and construct call chains to identify RCE vulnerabilities in frameworks; 2) a systematical prompt-based exploitation method to verify and exploit the found vulnerabilities in LLM-integrated apps. This technique involves various strategies to control LLM outputs, trigger RCE vulnerabilities and launch subsequent attacks. Our research has uncovered a total of 20 vulnerabilities in 11 LLM-integrated frameworks, comprising 19 RCE vulnerabilities and 1 arbitrary file read/write vulnerability. Of these, 17 have been confirmed by the framework developers, with 11 vulnerabilities being assigned CVE IDs. For the 51 apps potentially affected by RCE, we successfully executed attacks on 17 apps, 16 of which are vulnerable to RCE and 1 to SQL injection. Furthermore, we conduct a comprehensive analysis of these vulnerabilities and construct practical attacks to demonstrate the hazards in reality. Last, we propose several mitigation measures for both framework and app developers to counteract such attacks.

Key facts. Cited by 37 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Sep 2023.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 8; 2025: 14; 2026: 12.

37 papers.

Sort:

By area:

  1. 2023A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Uglycited by 387
  2. 2024IsolateGPT: An Execution Isolation Architecture for LLM-Based Agentic Systemscited by 130
  3. 2024AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathwayscited by 127
  4. 2024Making Them Ask and Answer: Jailbreaking Large Language Models in Few Queries via Disguise and Reconstructioncited by 107
  5. 2024AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attackscited by 96
  6. 2025Multi-Agent Systems Execute Arbitrary Malicious Codecited by 53
  7. 2023Privacy in Large Language Models: Attacks, Defenses and Future Directionscited by 51
  8. 2025EchoLeak: The First Real-World Zero-Click Prompt Injection Exploit in a Production LLM Systemcited by 29
  9. 2025Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competitioncited by 24
  10. 2024AI Risk Management Should Incorporate Both Safety and Securitycited by 17
  11. 2024Are LLMs Correctly Integrated into Software Systems?cited by 15
  12. 2025Secure and Efficient Access Control for Computer-Use Agents via Context Spacecited by 11
25 more
  1. 2025SandboxEval: Towards Securing Test Environment for Untrusted Codecited by 10
  2. 2025LLM Applications: Current Paradigms and the Next Frontiercited by 8
  3. 2025Breaking Agent Backbones: Evaluating the Security of Backbone LLMs in AI Agentscited by 8
  4. 2024A Jailbroken GenAI Model Can Cause Substantial Harm: GenAI-powered Applications are Vulnerable to PromptWarescited by 7
  5. 2025Bleeding Pathways: Vanishing Discriminability in LLM Hidden States Fuels Jailbreak Attackscited by 5
  6. 2025A First Look at the Security Issues in the Model Context Protocol Ecosystemcited by 5
  7. 2026CIBER: A Comprehensive Benchmark for Security Evaluation of Code Interpreter Agentscited by 5
  8. 2026No Attack Required: Semantic Fuzzing for Specification Violations in Agent Skillscited by 5
  9. 2023Survey on AI Ethics: A Socio-technical Perspectivecited by 4
  10. 2025Your Mailbox Is Mine: Prompt Injection Attacks Against Real-World LLM Email Agentscited by 2
  11. 2025Running in CIRCLE? A Simple Benchmark for LLM Code Interpreter Securitycited by 2
  12. 2025Securing Educational LLMs: A Generalised Taxonomy of Attacks on LLMs and DREAD Risk Assessmentcited by 2
  13. 2026AgentFlow: Building Agent Dependency Graphs for Static Analysis of Agent Programscited by 2
  14. 2026LogicHunter: Testing LLM Agent Frameworks with an Agentic Oraclecited by 2
  15. 2026Layered LLM Defenses as an Ensemble: Access Tiers, Inference Cost, and the Measured Failure Correlation Between Defense Layerscited by 2
  16. 2025LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systemscited by 1
  17. 2026Demystifying and Detecting Agentic Workflow Injection Vulnerabilities in GitHub Actionscited by 1
  18. 2026Refusal Evaluation in Coding LLMs and Code Agents: A Systematic Review of Thirteen Malicious-Code Prompt Corpora (2023-2025)cited by 1
  19. 2026Misleading the Planner through Deceptive Resumes: Registration-Time Injection in Centralized Multi-Agent Systemscited by 1
  20. 2024Exploring the Privacy Protection Capabilities of Chinese Large Language Modelscited by 0
  21. 2025SAP-DIFF: Semantic Adversarial Patch Generation for Black-Box Face Recognition Models via Diffusion Modelscited by 0
  22. 2026MOSAIC: Knowledge-Guided CLI Command Composition Attack in LLM Coding Agentscited by 0
  23. 2026DualView: Preventing Indirect Prompt Injection in Personal AI Agentscited by 0
  24. 2026From Prompt Injection to Web Exploitation: Revisiting Classic Vulnerabilities in LLM-Integrated Applicationscited by 0
  25. 2026Can Agents Trust Their Skills? Uncovering Unsafe Chains of Trust in Skill-Based LLM Agentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024From Vulnerabilities to Remediation: A Systematic Literature Review of LLMs in Code Security
  2. 2026Exploring Automated Vulnerability Identification in JavaScript Code Using Large Language Models
  3. 2026Poster: Rethinking Security in LLM Code Generation through Real-World Risk Scenarios
  4. 2026Guiding Symbolic Execution with Static Analysis and LLMs for Vulnerability Discovery
  5. 2026An Empirical Evaluation of LLM-Generated Code Security Across Prompting Methods
  6. 2026LLM-Enabled Open-Source Systems in the Wild: An Empirical Study of Vulnerabilities in GitHub Security Advisories
  7. 2026Taint-Style Vulnerability Detection and Confirmation for Node.js Packages Using LLM Agent Reasoning
  8. 2025When Code Crosses Borders: A Security-Centric Study of LLM-based Code Translation ↗