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RiskChainBench: A Benchmark for Obfuscated Platform Message Restoration and Evidence-Grounded Web Investigation — arXiv:2609.16900

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.16900
License (as archived): CC0-1.0
Archived: 2026-10-15T19:19:21.247Z
SHA-256 of the archived text: 126d336f398ec141281f03560ecc6b2ef9bfb9c53a80b800d5845e80f2f87768
Stable URL of this page: https://metraipolis.com/documento/dc_755aade189bf

How to cite

RiskChainBench: A Benchmark for Obfuscated Platform Message Restoration and Evidence-Grounded Web Investigation — arXiv:2609.16900 — arXiv (en), https://arxiv.org/abs/2609.16900 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_755aade189bf (Metraipolis archive dc_755aade189bf, sha256 126d336f398ec141281f03560ecc6b2ef9bfb9c53a80b800d5845e80f2f87768)

BibTeX

@misc{dc755aade189bf,
  title = {RiskChainBench: A Benchmark for Obfuscated Platform Message Restoration and Evidence-Grounded Web Investigation — arXiv:2609.16900},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.16900},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_755aade189bf; sha256 126d336f398ec141281f03560ecc6b2ef9bfb9c53a80b800d5845e80f2f87768},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.16900. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Platform abuse campaigns conceal redirection instructions with emojis, homophones, character decomposition, and redundant symbols, then route users through disguised links to services associated with pornography, fraud, gambling, or illicit transactions. Existing benchmarks evaluate obfuscated text and risky webpages separately, obscuring how target recovery affects downstream evidence acquisition. We introduce RiskChainBench, pairing 3,600 synthetic token-text restoration inputs from 600 source sessions with 600 corresponding human-labeled local web environments. A model first restores the message, operational intent, and destination; the same underlying model then acts as a VLM-driven web agent that investigates the correctly associated website and produces a frozen, evidence-cited risk report without message-side semantics or domain-reputation cues. We score restoration and correct-routing web investigation separately and compose them offline by applying the frozen primary-entry prediction as a gate to the same Task 2 result. Human labels determine task correctness, while a fixed multimodal evidence judge assesses faithfulness, sufficiency, completeness, and consistency. Across ten models, Entry Top-1 ranges from 35.2% to 95.2% and web decision accuracy from 26.3% to 62.8%; the leading systems differ across entry recovery, full reconstruction, website decisions, and fine-grained typing. Execution failures account for 31.9% of web runs, whereas post-decision type errors account for only 0.9%, identifying stable exploration and risk judgment as the principal bottlenecks. We release the benchmark, protocol, and resettable local sandbox.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-16 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2609.16900 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T18:32:32.839Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-09-16?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).