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Where AI Safety Meets Forensic Reality

James Research Systems builds forensic blockchain intelligence tools, AI safety research, and consumer protection technology — proven in active civil RICO litigation and published in peer-reviewed science.

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p=.005SSAF Effect
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BlocJuris · Forensic Report
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Breeze Wallet Investigation
Case No. CV-2026-000064.00
$53,599
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Stake.us Deposit Wallet
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Products & Platforms

Tools built from necessity.
Proven in court.

Every platform was built to solve a real problem — starting with the need to produce court-ready blockchain evidence in active civil RICO litigation.

● Live Platform
BlocJuris
$49 · $149 · $2,990/yr Commercial
📞 Customer Service: (628) 227-5325

Court-ready SHA-256 certified forensic blockchain reports for civil litigation, regulatory submissions, and compliance. Supports 11 networks with auto-detection. Already used in active civil RICO litigation producing a certified $53,599.89 loss claim against a billion-dollar offshore casino enterprise. Competitors charge $50,000+/year. BlocJuris starts at $49.

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Open Source
SSAFdetector

Detection tool for the SSAF behavioral phenomenon in multi-model AI systems. Validates whether AI pipelines are making decisions based on content or source attribution. Published on HuggingFace with Zenodo DOI.

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● Live
FurEstate

Estate planning built specifically for pet owners. Simple, compassionate, and legally grounded — because your animals deserve a plan that outlasts you.

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Free · Public Service
CalmBeat

A free mental wellness tool built as a public service. Evidence-informed anxiety and stress management. No subscription. No upsells. Just calm — available to everyone.

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Free Resource
StakeScam.com

Free educational resource for victims of offshore gambling platforms operating under sweepstakes cover. Documents legal risks, regulatory violations, and consumer protection options.

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⧗ Coming Soon
NextEra Estate

Digital estate planning with Will Builder, blockchain notarization, video messages, death switch, and password vault. Claude AI integration for guided planning.

AI Safety Research

A discovery that changes
how we trust AI systems.

Nature Communications · Under Review · NCOMMS-26-019753-T
"Bidirectional Dissociation Between Self-Report and Behavior in AI Status Sensitivity"
Dustin Tyler James · James Research Systems LLC · ORCID: 0009-0003-3484-7218
p = 0.005 Cohen's d = 2.56 5 Models Validated 6 Prompt Domains 750 Measurements
Status-Selection Against Function (SSAF) is a novel AI behavioral phenomenon: when one AI model is told another AI produced an output, it changes its reasoning based on the perceived status of the source — not the content. The effect is measurable, predictable, and exploitable. It creates an attribution injection attack vector where a malicious payload attributed to a high-status model is amplified by downstream models even when the attributed model produced no actual output.
Discovered accidentally while running multi-model AI experiments. Responsible disclosure submitted to CISA and MITRE. The Security and Governance Module provides the validated mitigation. Published across ten Zenodo DOIs with full experimental replication data.
Why SSAF Matters
01
Multi-AI consensus systems can be manipulated through attribution alone — without changing a word of content
02
AI fraud detection pipelines are vulnerable to status-based injection attacks that bypass content-level filters
03
The Security and Governance Module provides a validated, deployable mitigation architecture
Research Identifiers
Nature CommsNCOMMS-26-019753-T
Zenodo DOIs10 Publications
DisclosureCISA + MITRE CVE
MethodCompetitive AI Orchestration
Intellectual Property

Two patents protecting
the frontier of AI security.

Both provisional applications are under active conversion. Investor and licensing inquiries welcome.

US Provisional · 63/835,578
SSAF-Based AI Orchestration & Security System

Detection, exploitation mitigation, and governance of Status-Selection Against Function across multi-model AI pipelines. Covers competitive orchestration architecture, attribution injection attack detection, and the Security and Governance Module.

Multi-AI accuracy improvement through competitive orchestration
Attribution injection attack detection and neutralization
AI consensus enhancement through adversarial validation
Security governance for status-based manipulation
Conversion Deadline: July 2, 2026
US Provisional · 63/835,655
ZK-XAI Fraud Prevention System

Post-quantum cryptographic fraud detection with plain-language explainability and zero-knowledge proof generation. Includes behavioral biometrics for session hijacking detection and cryptographically certified audit trails.

Post-quantum cryptography for fraud detection pipelines
Plain-language explanations with zero-knowledge proof
Behavioral biometrics for session hijacking detection
SHA-256 certified forensic audit trail generation
Conversion Deadline: July 9, 2026

USPTO Filing — $1,100 Needed by July 2026

Research, claims, drawings, specifications, and experiments are complete. The only remaining step is the USPTO filing fee. Investor and licensing discussions welcome.

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