Versatile AI Risk Assessment

AI threat catalogueApplication and Integration SecurityProduction

Insecure Output Handling

The application passes an AI model's answers to downstream systems without checks, for example to databases, web pages or command lines. If the output contains malicious code or commands, the system executes them as if they came from a trusted source.

As of: July 2026 · Catalogue version 2026.07.17.3 · 4 mitigations · 8 verified sources

Description

Many AI applications do more than display model answers: they render them as web pages, build database queries from them or run generated program code. Because attackers can steer what the model outputs through their inputs, this gives them indirect access to those downstream systems. Without validation and context-aware encoding of the outputs, classic injection attacks emerge, in which attackers smuggle their own commands into a system: malicious scripts in the browser (XSS), manipulated database commands (SQL injection), and even attacker-supplied code running on the server (remote code execution). The risk grows when the AI application holds more privileges than the person operating it.

Possible impact

A successful attack gives attackers control over parts of the application: they read or alter data, hijack other users' sessions or run their own commands on the servers. For the organisation this means operational disruption, data leakage with notification duties under the GDPR and a loss of customer trust, even though the model itself worked flawlessly.

Example

A maths assistant runs model-generated program code directly on the server to solve calculation tasks. A user phrases the question so that the model produces malicious code; the server executes it and gives the attacker access to the system.

Recommended mitigations (4)

Every mitigation states its control type, effect, implementation level and the reason for the classification.

Framework mappings

Verified locations in OWASP, NIST AI RMF, MITRE ATLAS, the EU AI Act and further frameworks. The mappings are taxonomic, not evidence of compliance.

OWASP LLM Top 10 LLM05:2025NIST AI RMF Section 2.9MITRE ATLAS AML.T0048EU AI Act Article 9(1), 9(2)(a), 9(2)(d)BSI R16 · R6BIML BIML-LLM inference:8 · BIML78 system:9

Verified references (8)

Every reference states the framework, the exact location and the publishing organisation.

More entries from the topic group Application and Integration Security.

Insecure Tool Design Many AI applications act on their own through connected tools, for example sending emails or querying databases. If those tools are too … Application Vulnerabilities The software around the AI model, meaning its web interface, APIs and databases, has the same weaknesses as any other application. … RAG-Specific Attacks (Document Poisoning) Attackers plant prepared documents in the knowledge base an AI system draws on via RAG (retrieval-augmented generation, looking up internal …

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Cite this entry

For reports, policies or internal documents; the link leads directly to this entry.

“Insecure Output Handling”. Versatile AI Risk Assessment, AI threat catalogue, as of July 2026.
https://www.versatile-ai-risk-assessment.com/en/wissensbasis/threats/insecure-output-handling/

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