Unverified50% confidenceFactExact time
Constrained decoding filters candidate tokens through finite state machines or grammar rules; if only one legal token remains after filtering, the sampling strategy ceases to have any effect and the output path is uniquely determined.
1
Sources
50%
Confidence
Long-term
Relevance
10/29/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedFilter/detection-based defenses (such as scanning for phrases like 'ignore previous instructions') are structurally doomed to fail, because natural language has no syntax that distinguishes instructions from data.74% similarUnverifiedConstrained Decoding ensures valid syntactic paths by setting the probability of illegal tokens to zero before each sampling step, based on the content already generated and the target Schema constraints.73% similarUnverifiedConstrained decoding dynamically masks, at each sampling step, tokens that do not conform to the target grammar (such as a regular expression or context-free grammar), mathematically excluding illegal outputs.72% similarVerifiedConstrained decoding dynamically blocks candidate words that do not conform to the target grammar or schema when the model generates each token, ensuring the output structure is legal at the decoding level.71% similarUnverifiedBecause LLMs cannot strictly distinguish 'instructions' from 'data' the way traditional programs do, prompt injection is difficult to eradicate with a single filter and usually requires a combination of defense-in-depth measures such as bidirectional input-output detection, least privilege, and tool-call whitelists.71% similar
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