Demystifying RCE Vulnerabilities in LLM-Integrated Apps
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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.
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Citing papers per year: 2023: 3; 2024: 8; 2025: 14; 2026: 12.
37 papers.
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- 2025SAP-DIFF: Semantic Adversarial Patch Generation for Black-Box Face Recognition Models via Diffusion Models27 Feb 2025
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- 2024AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attacks2 Mar 2024
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- 2023Survey on AI Ethics: A Socio-technical Perspective28 Nov 2023
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- 2025Multi-Agent Systems Execute Arbitrary Malicious Codecited by 53
- 2025Secure and Efficient Access Control for Computer-Use Agents via Context Spacecited by 11
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- 2025Breaking Agent Backbones: Evaluating the Security of Backbone LLMs in AI Agentscited by 8
- 2025A First Look at the Security Issues in the Model Context Protocol Ecosystemcited by 5
- 2026CIBER: A Comprehensive Benchmark for Security Evaluation of Code Interpreter Agentscited by 5
- 2026No Attack Required: Semantic Fuzzing for Specification Violations in Agent Skillscited by 5
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12 most cited of 18 All 18 (JSON)
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- 2026From Prompt Injection to Web Exploitation: Revisiting Classic Vulnerabilities in LLM-Integrated Applications10 Aug 2026
- 2026LogicHunter: Testing LLM Agent Frameworks with an Agentic Oracle7 Jul 2026
- 2026DualView: Preventing Indirect Prompt Injection in Personal AI Agents4 Jul 2026
- 2026MOSAIC: Knowledge-Guided CLI Command Composition Attack in LLM Coding Agents3 Jul 2026
- 2026AgentFlow: Building Agent Dependency Graphs for Static Analysis of Agent Programs2 Jul 2026
- 2026Refusal Evaluation in Coding LLMs and Code Agents: A Systematic Review of Thirteen Malicious-Code Prompt Corpora (2023-2025)19 May 2026
- 2026No Attack Required: Semantic Fuzzing for Specification Violations in Agent Skills13 May 2026
- 2026Demystifying and Detecting Agentic Workflow Injection Vulnerabilities in GitHub Actions8 May 2026
- 2026CIBER: A Comprehensive Benchmark for Security Evaluation of Code Interpreter Agents23 Feb 2026
- 2025Breaking Agent Backbones: Evaluating the Security of Backbone LLMs in AI Agents26 Oct 2025
12 newest of 18 All 18 (JSON)
- 2024Making Them Ask and Answer: Jailbreaking Large Language Models in Few Queries via Disguise and Reconstructioncited by 107
- 2025EchoLeak: The First Real-World Zero-Click Prompt Injection Exploit in a Production LLM Systemcited by 29
- 2024A Jailbroken GenAI Model Can Cause Substantial Harm: GenAI-powered Applications are Vulnerable to PromptWarescited by 7
- 2025Bleeding Pathways: Vanishing Discriminability in LLM Hidden States Fuels Jailbreak Attackscited by 5
- 2025Securing Educational LLMs: A Generalised Taxonomy of Attacks on LLMs and DREAD Risk Assessmentcited by 2
- 2025EchoLeak: The First Real-World Zero-Click Prompt Injection Exploit in a Production LLM System6 Sep 2025
- 2025Securing Educational LLMs: A Generalised Taxonomy of Attacks on LLMs and DREAD Risk Assessment12 Aug 2025
- 2025Bleeding Pathways: Vanishing Discriminability in LLM Hidden States Fuels Jailbreak Attacks14 Mar 2025
- 2024A Jailbroken GenAI Model Can Cause Substantial Harm: GenAI-powered Applications are Vulnerable to PromptWares9 Aug 2024
- 2024Making Them Ask and Answer: Jailbreaking Large Language Models in Few Queries via Disguise and Reconstruction28 Feb 2024
- 2024Are LLMs Correctly Integrated into Software Systems?cited by 15
- 2024Are LLMs Correctly Integrated into Software Systems?6 Jul 2024
- 2023A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Uglycited by 387
- 2024AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathwayscited by 127
- 2024AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attackscited by 96
- 2023Privacy in Large Language Models: Attacks, Defenses and Future Directionscited by 51
- 2024AI Risk Management Should Incorporate Both Safety and Securitycited by 17
- 2025SandboxEval: Towards Securing Test Environment for Untrusted Codecited by 10
- 2023Survey on AI Ethics: A Socio-technical Perspectivecited by 4
- 2025Running in CIRCLE? A Simple Benchmark for LLM Code Interpreter Securitycited by 2
- 2026Layered LLM Defenses as an Ensemble: Access Tiers, Inference Cost, and the Measured Failure Correlation Between Defense Layerscited by 2
- 2025LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systemscited by 1
- 2024Exploring the Privacy Protection Capabilities of Chinese Large Language Modelscited by 0
- 2026Layered LLM Defenses as an Ensemble: Access Tiers, Inference Cost, and the Measured Failure Correlation Between Defense Layers28 Aug 2026
- 2025LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems12 Sep 2025
- 2025Running in CIRCLE? A Simple Benchmark for LLM Code Interpreter Security25 Jul 2025
- 2025SandboxEval: Towards Securing Test Environment for Untrusted Code27 Mar 2025
- 2024AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathways4 Jun 2024
- 2024AI Risk Management Should Incorporate Both Safety and Security29 May 2024
- 2024Exploring the Privacy Protection Capabilities of Chinese Large Language Models27 Mar 2024
- 2024AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attacks2 Mar 2024
- 2023A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly4 Dec 2023
- 2023Survey on AI Ethics: A Socio-technical Perspective28 Nov 2023
- 2023Privacy in Large Language Models: Attacks, Defenses and Future Directions16 Oct 2023
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