Unverified50% confidenceFactExact time
HoS's gates are learned rather than hard-coded rules; defenses such as ASIDE instead adopt architectural-level instruction-data separation.
1
Sources
50%
Confidence
Long-term
Relevance
10/28/2026
First Seen
Sources
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Related Claims
UnverifiedHard constraints in the decoding phase physically guarantee that outputs conform to predefined grammar or schemas by masking illegal vocabulary options during token generation (logit masking or structured sampling).61% 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.61% similarUnverifiedPrompt injection is difficult to defend against with traditional firewalls because it does not rely on code vulnerabilities, but exploits the model's tendency to obey natural-language instructions, and its attack surface completely overlaps with normal functional input.59% similarUnverifiedThe fundamental logic of defending against indirect injection is the separation of data and instructions: retrieved content should be confined to a read-only data zone, and hierarchical isolation can be established by placing external content in the tool role rather than the user role.58% similarUnverifiedThe capability boundaries of intent recognition systems are determined by manually written rules and have limited scalability, whereas large language models, through pretraining on massive text, can handle new tasks that were not explicitly programmed, achieving a paradigm shift from rule-driven to data-driven approaches.58% similar
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