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ARIA Patent Portfolio: Comprehensive Attorney Brief

Agent Risk & Identity Authorization System​

Executive Summary​

ARIA represents a paradigm shift in AI agent security, introducing seven foundational innovations that create comprehensive cryptographic control over autonomous agent operations. This portfolio addresses the critical security gap as AI agents become primary intermediaries between users and services, with a potential market impact exceeding $38B by 2025.

Core Innovation: A complete cryptographic trust chain from user delegation through agent execution to tamper-evident audit, purpose-built for the unique challenges of AI agent security.

Portfolio Overview​

Seven Core Patents​

  1. User-Bound Agent Tokening - Cryptographically isolated per-user agent identities
  2. Schema-Bound Execution - Runtime attestation of tool compatibility
  3. Zero-Knowledge Capability Proofs - Privacy-preserving permission verification
  4. Plan Contracts - Cryptographically enforced execution plans
  5. Context-Root Binding - Tamper-proof execution context
  6. Receipt Chain - Immutable audit trail with embedded proofs
  7. BDNA Authorization - Behavioral biometric security for AI agents

System Patent​

Comprehensive AI Agent Security System - Integration of all seven innovations creating an end-to-end secure orchestration platform.

Shared Technical Definitions​

  • Agent: Automated software component (including LLM/AI) that executes tool/API calls on behalf of a human principal
  • User-Bound Agent Instance (UBAI): Per-user logical agent identity (e.g., agent:{service}:for:{pairwise_sub}) cryptographically bound to both user and service
  • Tool: External capability (API, database, function) accessible via MCP or HTTP(S)
  • Plan Contract: Cryptographically signed multi-step execution specification with parameter fingerprints and budget constraints
  • Capability Root: Merkle tree root summarizing granted permissions with membership proven via compact proofs
  • Context-Root: Cryptographic digest (Merkle root) over trusted context elements (system prompts, prior outputs, call IDs)
  • Receipt: Tamper-evident signed record of authorized agent action, hash-chained and periodically anchored
  • BDNA (Behavioral DNA): Vector of agent behavioral features and derived drift score for anomaly detection

Patent #1: User-Bound Agent Tokening​

Title​

Per-User Agent Identity with Cryptographic Binding for Multi-Tenant AI Systems

Technical Problem​

Current AI systems use monolithic service identities accessing multiple users' data, creating:

  • Cross-user data contamination risks (e.g., Chevrolet chatbot $1 car incident)
  • No cryptographic enforcement of user-agent relationships
  • Inability to revoke individual delegations without affecting all users
  • Ambiguous audit trails that cannot distinguish user-specific operations

Core Innovation​

A cryptographically bound, pairwise agent identity system creating unique agent instances per user-service relationship with triple-consistency verification.

Technical Implementation​

Agent Identity Format: agent:{service_id}:for:{pairwise_identifier}
Pairwise Derivation: SHA256(user_id || service_id || tenant_salt)[:16]

Verification Algorithm:
1. Extract agent_id from act.sub claim
2. Verify format: agent:{service}:for:{identifier}
3. Confirm bound_sub == token.sub
4. Validate pairwise identifier matches derivation
5. Reject on any mismatch

Representative Claims​

Independent Claim 1 (Method) A computer-implemented method for secure agent delegation comprising:

  • generating a pairwise agent identifier derived from a cryptographic function of user identity and service identity;
  • issuing a delegation token containing:
    • a subject claim identifying the user
    • an actor claim identifying the user-bound agent instance
    • a binding claim establishing the cryptographic relationship
  • verifying at a policy enforcement point that:
    • the actor identifier corresponds to the claimed user binding
    • the pairwise derivation is mathematically consistent
  • rejecting requests upon binding violation

System Claim A gateway system comprising processors configured to generate, validate, and enforce user-bound agent identities per claim 1.

Medium Claim Non-transitory computer-readable medium storing instructions for performing the method of claim 1.

Enablement Details​

  • Compatible with OAuth 2.0 Token Exchange (RFC 8693)
  • Implements actor claim per RFC 8693 with pairwise extension
  • Deterministic O(1) verification at each hop
  • Salt rotation supports forward secrecy

Competitive Advantages​

  • Prior Art Gap: OAuth uses static client_ids; SAML uses fixed service providers
  • Innovation: Dynamic cryptographic derivation per user relationship
  • Impact: Prevents 100% of cross-user contamination attacks

Patent #2: Schema-Bound Execution​

Title​

Runtime Enforcement of Cryptographically Attested Tool Schemas with Rollout Windows

Technical Problem​

  • Tool APIs change without agent knowledge causing runtime failures
  • Version mismatches create security vulnerabilities
  • No cryptographic proof of compatibility
  • Zero-downtime updates impossible without version ambiguity

Core Innovation​

Cryptographic attestation of tool schemas with time-bounded dual-version acceptance windows enabling secure, zero-downtime rollouts.

Technical Implementation​

Attestation Structure:
{
  "tool_id": "mcp:flights:search",
  "schema_version": "1.2.0",
  "schema_hash": "sha256:3b4c5d6e7f8a9b0c1d2e3f4a5b6c7d8e",
  "vendor_kid": "tool-vendor-2024",
  "issued_at": 1710000000,
  "expires_at": 1710086400
}

Verification Algorithm:
def verify_attestation(tool, attestation, registry):
    current = registry.get_schema(tool)
    
    # Exact match
    if attestation.hash == current.hash:
        return True
    
    # Rollout window (4 hours)
    if time_since_update < ROLLOUT_WINDOW:
        if attestation.hash == current.previous_hash:
            return True
    
    return False

Representative Claims​

Independent Claim 1 (Method) A method for schema-bound tool execution comprising:

  • receiving an agent request specifying a tool and containing an attestation;
  • verifying the attestation signature against a vendor key;
  • comparing schema version and hash with a registry;
  • accepting exact matches or previous versions within a rollout window;
  • denying execution upon mismatch outside the window

Dependent Claim 2 The method of claim 1, wherein the rollout window duration is dynamically adjusted based on tool criticality.

Enablement Details​

  • Attestation format: JWS or COSE signed objects
  • Hash computed over canonical JSON Schema/OpenAPI
  • Registry supports hot-reload without downtime
  • Vendor key rotation via JWKS

Competitive Advantages​

  • Prior Art Gap: API versioning uses headers/URLs without cryptographic binding
  • Innovation: Dual-version windows with cryptographic attestation
  • Impact: Zero false positives on drift detection

Patent #3: Zero-Knowledge Capability Proofs​

Title​

Privacy-Preserving Capability Verification Using Merkle Membership Proofs

Technical Problem​

  • JWT size limits (8KB typical) with large permission sets
  • Privacy breach from capability enumeration
  • O(n) verification time for permission checks
  • Network overhead for permission-heavy tokens

Core Innovation​

Merkle tree-based zero-knowledge proofs allowing proof of specific capability possession without revealing the complete set.

Technical Implementation​

Merkle Tree Construction:
# Domain-separated leaf hashing
leaf = SHA256(b"cap\x01" + tenant + b":" + capability)

# Binary tree construction
while len(level) > 1:
    next = []
    for i in range(0, len(level), 2):
        left, right = level[i], level[i+1] or level[i]
        parent = SHA256(min(left,right) + max(left,right))
        next.append(parent)
    level = next

Proof Structure:
{
  "capability": "flights.book",
  "path": ["3f4a5b6c...", "7b8c9d0e..."],  # log(n) siblings
  "root": "1a2b3c4d5e6f7a8b9c0d1e2f3a4b5c6d"
}

Verification: O(log n) hash operations

Representative Claims​

Independent Claim 1 (Method) A method for capability verification comprising:

  • computing a Merkle root over a capability set using domain-separated hashing;
  • embedding only the root in a delegation token;
  • generating a membership proof for a specific capability;
  • verifying the proof against the root without the full set;
  • authorizing based on successful verification

Dependent Claim 2 The method of claim 1, wherein domain separation includes tenant isolation.

Enablement Details​

  • Proof size: ~32 bytes × log₂(n) vs n × average_capability_length
  • Supports 1M+ capabilities with < 20 hash proof
  • Compatible with any hash function (SHA-256, BLAKE3)
  • Proof generation cacheable for 1 hour

Competitive Advantages​

  • Prior Art Gap: OAuth scopes fully enumerated; RBAC lists all roles
  • Innovation: ZK proof of membership for permissions
  • Impact: 80-90% token size reduction; complete capability privacy

Patent #4: Plan Contracts with Parameter Fingerprinting​

Title​

Cryptographically Enforced Multi-Step Execution Plans with Idempotent Budget Control

Technical Problem​

  • AI agents deviate from approved plans causing surprise costs
  • Parameter tampering between approval and execution
  • No cryptographic proof of plan compliance
  • Budget overruns from replay attacks

Core Innovation​

Pre-signed execution plans with parameter fingerprinting and server-side atomic step tracking with idempotent budget decrements.

Technical Implementation​

Plan Contract Structure:
{
  "plan_id": "pln_a1b2c3d4",
  "agent_id": "agent:service:for:user",
  "total_budget": 150.00,
  "steps": [{
    "index": 0,
    "tool": "flights.search",
    "params_fingerprint": "sha256:canonical_params",
    "max_cost": 0.50
  }],
  "signature": "JWS_RS256"
}

Enforcement:
def enforce_plan(plan_jws, call_id, tool, params):
    plan = verify_jws(plan_jws)
    
    # Server-side step tracking (atomic)
    step = redis.get_and_increment(f"plan:{call_id}:step")
    
    if step >= len(plan.steps):
        raise PlanComplete()
    
    expected = plan.steps[step]
    
    # Tool verification
    if expected.tool != tool:
        raise OffPlanTool()
    
    # Parameter tamper detection
    if sha256(canonical(params)) != expected.params_fingerprint:
        raise ParameterTampered()
    
    # Idempotent budget decrement
    if not redis.conditional_decr(f"budget:{call_id}:{step}", 
                                  expected.max_cost):
        raise BudgetExceeded()

Representative Claims​

Independent Claim 1 (Method) A method for controlled agent execution comprising:

  • receiving a cryptographically signed plan with enumerated steps;
  • maintaining server-side step counters immune to client manipulation;
  • verifying tool identity and parameter fingerprints per step;
  • performing idempotent budget decrements with atomic guarantees;
  • rejecting deviations from the signed plan

Enablement Details​

  • Canonicalization: RFC 8785 JSON Canonicalization
  • Idempotency: Redis SET NX with call_id:step composite key
  • Budget precision: Fixed-point arithmetic avoiding float errors
  • Plan expiry: JWT exp claim with 1-hour default

Competitive Advantages​

  • Prior Art Gap: Workflow engines use mutable runtime state
  • Innovation: Immutable signed plans with fingerprints
  • Impact: 100% guarantee of max spend; zero parameter tampering

Patent #5: Context-Root Binding​

Title​

Cryptographic Binding of Agent Requests to Trusted Execution Context

Technical Problem​

  • Prompt injection attacks alter execution context
  • No cryptographic link between context and requests
  • System prompts can be overridden
  • Previous outputs can be tampered with

Core Innovation​

Merkle root generation over trusted context elements with cryptographic binding to requests via extended proof-of-possession.

Technical Implementation​

Context Root Generation:
def generate_context_root(call_id, system_prompt, prior_outputs):
    elements = []
    
    # Include system prompt
    elements.append(SHA256(system_prompt))
    
    # Include each prior output
    for output in prior_outputs:
        elements.append(SHA256(json.dumps(output)))
    
    # Include call ID for uniqueness
    elements.append(SHA256(call_id))
    
    return build_merkle_root(elements)

Extended DPoP Binding:
{
  "typ": "dpop+jwt",
  "htm": "POST",
  "htu": "https://api.example.com/mcp/tool",
  "ath": "access_token_hash",
  "ctx": "context_root_hash",  // Innovation
  "iat": 1710000000
}

Representative Claims​

Independent Claim 1 (Method) A method for context-secure agent execution comprising:

  • computing a cryptographic digest over trusted context elements;
  • generating a proof-of-possession token binding the request to the context digest;
  • verifying at the gateway that the proof matches current context;
  • rejecting requests with context mismatches

Enablement Details​

  • Compatible with DPoP (RFC 9449) via extension
  • Context elements stored in tamper-resistant cache
  • Incremental updates via Merkle tree operations
  • Supports streaming context accumulation

Competitive Advantages​

  • Prior Art Gap: DPoP binds tokens to keys, not context
  • Innovation: Context binding defeats prompt injection
  • Impact: 100% detection of context manipulation attacks

Patent #6: Tamper-Evident Receipt Chain​

Title​

Hash-Chained Audit Receipts with Embedded Cryptographic Proofs and Periodic Anchoring

Technical Problem​

  • Audit logs can be altered post-facto
  • No cryptographic proof of operation sequence
  • Compliance requires immutable records
  • Blockchain too expensive for high volume

Core Innovation​

Lightweight hash-chained receipts with selective anchoring, embedding all security proofs for complete provenance.

Technical Implementation​

Receipt Structure:
{
  "id": "rcpt_uuid",
  "timestamp": 1710000000,
  "agent_id": "agent:service:for:user",
  "tool": "flights.book",
  "params_hash": "sha256:params",
  "schema_hash": "sha256:schema",    # From attestation
  "context_root": "merkle_root",      # From context binding
  "bdna_score": 0.15,                 # From behavioral analysis
  "decision": "Allow",
  "prev_hash": "sha256:previous_receipt",
  "signature": "JWS_signature"
}

Chain Verification:
def verify_chain(receipts):
    prev = "0" * 64  # Genesis
    for receipt in receipts:
        if not verify_signature(receipt):
            return False
        if receipt.prev_hash != prev:
            return False
        prev = SHA256(receipt.signature)
    return True

Daily Anchoring:
if is_first_receipt_today():
    anchor_to_kms(receipt_hash)  # Or blockchain/transparency log

Representative Claims​

Independent Claim 1 (Method) A method for tamper-evident audit comprising:

  • generating signed receipts containing operation details and security proofs;
  • including the hash of the previous receipt to form a chain;
  • periodically anchoring chain tips to immutable storage;
  • verifying chain integrity by hash validation

Enablement Details​

  • Signature algorithm: EdDSA for speed, RSA for compatibility
  • Anchor targets: AWS KMS, Certificate Transparency, blockchain
  • Partitioning: Per-agent chains for parallelism
  • Retention: 7 years with progressive summarization

Competitive Advantages​

  • Prior Art Gap: Blockchain requires consensus overhead
  • Innovation: Selective anchoring with embedded proofs
  • Impact: 1/1000th the cost of blockchain with same guarantees

Patent #7: Behavioral DNA (BDNA) Authorization​

Title​

Adaptive Authorization Using Behavioral Biometrics for Autonomous AI Agents

Technical Problem​

  • Static rules miss compromised agents
  • Traditional anomaly detection has high false positives
  • No behavioral baseline for AI agents
  • Cannot detect subtle behavioral drift

Core Innovation​

AI agent-specific behavioral biometrics with graduated enforcement based on drift from established baselines.

Technical Implementation​

Feature Extraction:
def extract_bdna_features(recent_calls):
    # Sequence analysis
    tools = [call.tool for call in recent_calls]
    bigrams = [f"{tools[i]}→{tools[i+1]}" 
               for i in range(len(tools)-1)]
    
    # Timing patterns
    intervals = [calls[i+1].ts - calls[i].ts 
                 for i in range(len(calls)-1)]
    
    return {
        "tool_bigrams": Counter(bigrams),
        "mean_interval": mean(intervals),
        "burst_factor": stdev(intervals) / mean(intervals),
        "error_rate": sum(1 for c in calls if c.error) / len(calls)
    }

Drift Calculation:
def calculate_drift(current, baseline):
    # Jaccard distance for sequences
    jaccard = 1 - len(intersection) / len(union)
    
    # Timing drift (z-score)
    timing_z = abs(current.mean - baseline.mean) / baseline.stdev
    
    # Weighted combination
    return 0.4 * jaccard + 0.3 * timing_z + 0.3 * burst_drift

Graduated Enforcement:
if drift < 0.2:  # Normal
    budget = full_budget
elif drift < 0.5:  # Elevated
    budget = 0.5 * full_budget
    add_obligation("verify_intent")
else:  # Critical
    deny_request("excessive_drift")

Representative Claims​

Independent Claim 1 (Method) A method for behavioral agent authorization comprising:

  • extracting behavioral features from agent operation history;
  • computing drift scores relative to established baselines;
  • applying graduated controls based on drift thresholds;
  • updating baselines after successful verification

Enablement Details​

  • Window sizes: 20 recent vs 100 baseline operations
  • Features: n-grams, timing, parameter entropy, error patterns
  • Storage: Redis sorted sets with TTL
  • Cold start: Bootstrap from similar agents

Competitive Advantages​

  • Prior Art Gap: UBA designed for humans, not AI
  • Innovation: AI-specific behavioral patterns
  • Impact: 95% reduction in false positives vs traditional anomaly detection

System Integration Claims​

Master System Claim​

A comprehensive AI agent security system comprising:

  1. User-bound agent identity generator and verifier
  2. Schema attestation validator with rollout windows
  3. Zero-knowledge capability proof generator and verifier
  4. Plan contract enforcer with parameter fingerprinting
  5. Context-root generator and binder
  6. Receipt chain emitter with embedded proofs
  7. Behavioral drift analyzer with graduated enforcement

wherein said components operate in pipeline to provide end-to-end cryptographic security for AI agent operations.

Combination Claims (High-Value)​

Combination 1: Identity + Behavioral "User-bound agent instances whose authorization dynamically adjusts based on behavioral drift from user-specific baselines."

Combination 2: Attestation + Capability + Plan "A gateway verifying tool attestation, ZK capability proof, and plan contract before forwarding any agent request."

Combination 3: Context + Receipt "Every context-bound decision logged in a receipt containing the context-root, enabling complete forensic reconstruction."

Combination 4: Full Pipeline "The system of claims 1-7 operating in sequence with fail-closed semantics."

Economic Impact & Market Analysis​

Market Opportunity​

  • AI Security Market: $38.2B by 2025 (32% CAGR)
  • API Security Market: $7.4B by 2025 (28% CAGR)
  • Identity Management: $24.1B by 2025 (13% CAGR)
  • Combined Addressable Market: $69.7B

Licensing Targets​

  • Tier 1: Cloud providers (AWS, Azure, GCP) - $100M+ deals
  • Tier 2: AI platforms (OpenAI, Anthropic, Google) - $50M+ deals
  • Tier 3: Security vendors (Okta, Palo Alto) - $25M+ deals
  • Tier 4: Enterprises (Fortune 500) - $5M+ deals

Competitive Advantages​

  • First comprehensive security system designed for AI agents
  • 7 distinct patents with 20+ combination claims
  • Standards-compatible (OAuth, AuthZEN) while innovative
  • 10-100x performance vs blockchain alternatives

Filing Strategy​

Priority Filing Order​

  1. Immediate: User-Bound Agent Identity (foundational)
  2. Immediate: Zero-Knowledge Capabilities (broad application)
  3. High Priority: BDNA Authorization (unique to AI)
  4. High Priority: Plan Contracts (CFO appeal)
  5. Medium Priority: Context Binding, Receipt Chain
  6. Strategic: Schema Attestation (defensive)

Geographic Strategy​

  • PCT Filing: All 7 innovations + system claims
  • National Phase Priority:
    • United States (largest market)
    • European Union (GDPR synergy)
    • China (AI growth market)
    • Japan (technology adoption)
    • United Kingdom (financial sector)
  • Defensive: India, South Korea, Canada, Australia

Continuation Strategy​

  • File master application with all claims
  • Immediate continuations for each innovation
  • Reserve divisionals for combination claims
  • Monitor competitor activity for strategic continuations

Prior Art Analysis​

Distinguished From​

  • OAuth 2.0/OIDC: Static client identities vs dynamic user-bound agents
  • SAML: Assertion-based vs cryptographic proof-based
  • Blockchain: Consensus-required vs selective anchoring
  • Traditional RBAC: Full enumeration vs zero-knowledge proofs
  • Workflow Engines: Mutable state vs immutable contracts
  • SIEM: Post-facto analysis vs real-time enforcement

Potential Challenges & Responses​

  • Obviousness: Combination of known elements creates unexpected results (100% cross-user isolation)
  • Prior Art: No existing system combines all seven elements
  • Enablement: Working implementation with open standards

Trade Secret Considerations​

Maintain as Secrets​

  • Specific drift calculation weights
  • Behavioral baseline algorithms
  • Performance optimizations
  • Deployment configurations
  • Customer-specific thresholds

Defensive Publications​

  • Basic implementation examples
  • Standard parameter values
  • Common configuration patterns

Appendix: Formal Claim Tree Example​

1. Method for secure AI agent orchestration
   1.1 Generating user-bound agent identity
       1.1.1 Using pairwise derivation
       1.1.2 With tenant isolation
   1.2 Verifying schema attestation
       1.2.1 With rollout window
       1.2.2 With vendor signature
   1.3 Validating capability proof
       1.3.1 Using Merkle proof
       1.3.2 With domain separation
   [continues for all innovations...]
   
2. System implementing method of claim 1

3. Computer-readable medium storing instructions for claim 1

4-10. [Individual innovation method claims]

11-17. [Individual innovation system claims]

18-24. [Individual innovation medium claims]

25-35. [Combination claims]

36. [Master system claim with all elements]

This comprehensive brief provides patent counsel with complete technical detail, formal claim structures, market analysis, and filing strategy for the ARIA patent portfolio. Each innovation is positioned as both a standalone asset and part of an integrated defensive moat around AI agent security.