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Could unsafe file uploads introduce security risks?
Could unsafe file uploads introduce security risks?
AI systems that ingest or process uploaded files, such as PDFs, Word documents, images, or code, are vulnerable to multiple attack vectors:
- Malware & Embedded Scripts: Uploaded files may contain malicious payloads, macros, or hidden code that executes during parsing or rendering.
- Indirect Prompt Injection: Hidden instructions embedded in file content can manipulate LLM behavior when the content is passed as input for summarization, Q&A, or reasoning.
- Malformed Files & Deserialization: Crafted file formats can trigger crashes or bypass input validation, potentially leading to remote code execution or model corruption.
These threats are particularly relevant when files are processed automatically by LLMs or downstream tools, often without human review.
If you answered Yes then you are at risk
If you are not sure, then you might be at risk too
Recommendations
- Validate file types, sizes, and content strictly, use allowlists and reject unsupported or dangerous formats.
- Sanitize and normalize file content before passing it to downstream components or LLMs.
- Scan all files for malware using antivirus and static analysis tools.
- Avoid feeding raw file content directly to language models, wrap it with safety context and monitor outputs.
- Use sandboxed or containerized environments for file parsing, summarization, or code execution.
- Monitor for patterns of indirect prompt injection in document content.
- If supporting file-based inputs in a RAG pipeline or agentic system, implement retrieval sanitation and memory protection.