GCD AI RADAR
incidents/AIR-2026-0350

RCE Vulnerabilities in LLM-Integrated Applications via Prompt Injection

Attack on AI1 Research / proof of conceptPrompt injectionInfrastructure vulnerability

Researchers identified 20 remote code execution vulnerabilities across 11 different LLM frameworks. The study showed that applications built with these frameworks could be exploited using prompt injection.

Details

type
Attack on AI
An AI model, agent or AI-powered product was the thing exploited, abused or leaked.
categories
  • Prompt injectionprimary
    Untrusted content (web pages, documents, emails, tool output) steers a model into ignoring its instructions.
  • Infrastructure vulnerability
    CVEs and advisories in AI frameworks, inference servers, MCP servers, AI developer tools and hosting stacks.
owasp llm top 10
mitre atlas
Both rows are derived from the type and categories (OWASP 2025 edition, ATLAS release 2026.09), not classified per record. How
severity
1 · Research / proof of concept
Demonstrated by researchers; no known real-world exploitation.
targets
Framework / tooling
products
LangChain, LlamaIndex, Langflow, Auto-GPT
incident date
27 Feb 2025
first reported
31 Mar 2026
origin
MITRE ATLAS
Derived from MITRE ATLAS (Apache 2.0, © MITRE).
classifier confidence
0.95 · published at or above 0.80; see the methodology
tags
rce · frameworks · prompt-injection
record
created 2026-10-18 06:55Z · updated 2026-10-18 06:55Z · json

Sources · 3

  1. 2026-03-31
    LLMSmith: RCE Vulnerabilities in LLM-Integrated Applications
    MITRE ATLAS · atlas.mitre.org/studies/AML.CS0052
  2. 2026-03-31
    Demystifying RCE Vulnerabilities in LLM-Integrated Apps
    arXiv · arxiv.org/abs/2309.02926
  3. 2026-03-31
    LLMSmith Website
    sites.google.com · sites.google.com/view/llmsmith

Titles and links belong to their publishers. The summary above is written by GCD AI RADAR in its own words; data is CC BY-SA 4.0.

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