SWAN uses AMR to embed semantic watermarks that persist through paraphrases, matching SOTA detection on original text and improving AUC by 13.9 points on paraphrased RealNews data.
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A watermark for large language models.arXiv preprint arXiv:2301.10226, 2023a
15 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 15representative citing papers
A new 7x4 taxonomy organizes agentic AI security threats by architectural layer and persistence timescale, revealing under-explored upper layers and missing defenses after surveying 116 papers.
ExaGPT uses span-level similarity retrieval from human and LLM datastores to detect machine-generated text while supplying the matching spans as human-interpretable evidence, achieving up to 37-point accuracy gains over prior interpretable detectors at 1% FPR.
Tool-using LLM agents can implement undetectable stegosystems, shifting the primary barrier to covert multi-agent collusion from technical feasibility to coordination without explicit agreement.
Interaction-layer antidistillation watermarks use system-prompt-induced behavioral markers like explicit follow-up questions that transfer to distilled student models at 45-89% relative fidelity and can be audited via black-box LLM-as-judge queries.
TextSeal provides a localized, distortion-free LLM watermark that outperforms baselines in detection strength, remains effective in mixed human-AI text, preserves model performance, and transfers through distillation for provenance tracking.
Existing LLM watermarking schemes can be defeated by semantic-preserving attacks including lexical changes, machine translation, and neural paraphrasing.
Response times modeled as drift-diffusion processes enable consistent estimation of population-average preferences from heterogeneous anonymous binary choices.
Standard deviation distributions of attention matrices in LLMs remain distinctive and stable after continued training, enabling fingerprinting to trace model lineage and detect potential plagiarism such as in Pangu Pro MoE.
Recursive paraphrasing attacks substantially lower detection rates for multiple AI text detectors with only minor quality loss, while a theoretical analysis ties best-case AUROC to total variation distance between human and AI distributions.
Autonomous AI cyber systems deployed by civilians fail the one-causal-step and integral-part requirements of the IHL direct participation test because harm arises from post-disengagement system decisions.
SCI-Defense combines perplexity detection, semantic integrity scoring across four manipulation dimensions, and inter-candidate detection to counter GEO attacks, reporting perfect precision on Amazon product data but domain-limited recall on web passages.
Chained rewrites by open-weight LLMs reduce watermark detection on diffusion LM outputs from 87.9% to 4.86% after five steps across multiple styles and models.
LLMSniffer improves detection of LLM-generated code on GPTSniffer and Whodunit benchmarks by fine-tuning GraphCodeBERT via two-stage supervised contrastive learning plus preprocessing and MLP classification.
Shared task findings show near-perfect binary detection of AI-generated text but greater difficulty in attributing outputs to particular language models.
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Direct Causation in International Humanitarian Law and the Challenge of AI-Mediated Civilian Cyber Operations
Autonomous AI cyber systems deployed by civilians fail the one-causal-step and integral-part requirements of the IHL direct participation test because harm arises from post-disengagement system decisions.