Verified65% confidenceFactExact time
Prompt injection exploits a fundamental flaw: the blurred boundary between "data" and "instructions"
3
Sources
65%
Confidence
Long-term
Relevance
10/2/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedLLM architectures cannot strictly distinguish between 'instructions' and 'data' at the semantic level, which is the root cause of why prompt injection is difficult to fundamentally eliminate.73% similarVerifiedPrompt injection is essentially an instruction hijacking attack that exploits the structural flaw of LLMs being unable to distinguish system instructions from user data at the semantic level, and is conceptually similar to SQL injection.72% similarUnverifiedTool-calling judge models have a systematic flaw: when no source for a value can be found in the input, the judge is more inclined to reject it even if the value is completely correct, a phenomenon named the "derivation gap".72% similarUnverifiedThe same type of error may correspond to completely different causes: unclear instructions and retrieval that fails to cover key documents both manifest as "answering the wrong question"; lacking real-time values and lacking private knowledge both manifest as "fabricating content"; insufficient base model capability and noisy training data labels may also produce seemingly similar errors.71% similarUnverifiedPoorly worded prompts may cause unstable AI output, which in turn causes downstream nodes to error out due to data format mismatch.71% similar
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