Secure Intents: A Cryptographic Framework for Autonomous Agent Coordination

Kwame Bryan
Email: kwame.bryan@gmail.com


Working Draft

Abstract. A purely cryptographic solution for autonomous agent coordination would allow agents to interact directly without relying on trusted intermediaries for verification. Digital signatures provide part of the solution, but the main benefits are lost if a trusted party is still required to verify agent authenticity, prevent replay attacks, and ensure decision privacy. We propose a solution to the autonomous agent trust problem using cryptographic composition of digital signatures, public-key encryption, temporal validity constraints, and execution metadata. The network timestamps agent intents by including them in an ongoing chain of cryptographic proofs, forming a record that cannot be altered without redoing the computational work.

1. Introduction

The proliferation of autonomous agents in decentralised systems has created a fundamental coordination problem. Current approaches rely on centralised verification authorities or assume agents operate in trusted environments. What is needed is a cryptographic system that allows agents to prove the authenticity, integrity, and temporal validity of their intentions without requiring a trusted third party.

Consider a typical autonomous agent interaction: Agent A generates a trading decision and communicates it to Execution System B. Under current architectures, System B must trust that: (1) the decision actually originated from Agent A, (2) the decision has not been tampered with during transmission, (3) the decision reflects current market conditions, and (4) sensitive strategy information remains confidential.

The conventional solution involves authentication servers, API keys, and access control lists. But this approach fails to scale across heterogeneous systems and creates single points of failure. We propose instead a cryptographic framework where agents can create verifiable proofs of their intentions.

2. Autonomous Agent Transactions

We define an intent as a structured message containing an agent's decision and the context necessary for execution. An autonomous agent is any system capable of generating intents independently based on environmental inputs and pre-programmed objectives.

The fundamental problem is establishing cryptographic guarantees for agent-generated intents across four dimensions:

  1. Authenticity: Proof that an intent originated from a specific agent
  2. Integrity: Assurance that an intent has not been modified after creation
  3. Confidentiality: Protection of sensitive decision parameters from unauthorized access
  4. Freshness: Verification that an intent reflects current system state

Traditional approaches address these properties independently, if at all. Our framework addresses all four simultaneously through cryptographic composition.

3. The Secure Intent Framework

3.1 Mathematical Foundation

Definition 3.1 (Secure Intent). A secure intent is a cryptographic construct defined by the composition:

SecureIntent = (I, σ) where I = EKR(P) || T || M and σ = SignA(I)

Notation:

Definition 3.2 (Intent Validity). An intent (I, σ) is valid at time t if and only if:

  1. VerifyA(I, σ) = 1 (signature verification succeeds)
  2. t ≤ T (temporal validity constraint)
  3. Parse(M) ≠ ⊥ (metadata is well-formed)

3.2 Cryptographic Primitives

Definition 3.3 (Digital Signature Scheme). A digital signature scheme consists of algorithms (KeyGen, Sign, Verify) where:

Definition 3.4 (Public-Key Encryption). A public-key encryption scheme consists of algorithms (Gen, Enc, Dec) where:

3.3 Formal Security Properties

Property 3.1 (Unforgeability). No polynomial-time adversary A can forge valid intents for honest agents with non-negligible probability:

Pr[ForgeA(λ) = 1] ≤ negl(λ)

where ForgeA(λ) is the forgery game where A produces (I*, σ*) such that VerifyA(I*, σ*) = 1 without access to agent A's signing key.

Property 3.2 (Semantic Security). The encryption scheme provides semantic security under chosen-plaintext attack (IND-CPA):

Pr[IND-CPAA(λ) = 1] ≤ 1/2 + negl(λ)

Property 3.3 (Temporal Binding). For any intent (I, σ) with embedded timestamp T, verification fails deterministically for all t > T:

∀t > T: ValidateIntent(I, σ, t) = 0

3.4 Intent Lifecycle

  1. Generation: Agent A creates decision payload based on environmental analysis
  2. Encryption: Payload is encrypted using recipient's public key KR
  3. Composition: Encrypted payload, TTL, and metadata are concatenated
  4. Signing: Complete intent structure is signed using Agent A's private key
  5. Transmission: Signed intent is transmitted to execution system
  6. Verification: Recipient validates signature, TTL, and decrypts payload
  7. Execution: Valid intents are executed according to contained instructions

4. Security Analysis

4.1 Threat Model

Definition 4.1 (Adversarial Model). We consider a polynomial-time adversary A that can:

Assumption 4.1. The underlying cryptographic primitives are secure:

4.2 Formal Security Theorems

Theorem 4.1 (Intent Authenticity). Under Assumption 4.1, no polynomial-time adversary can forge valid intents for uncorrupted agents with probability greater than negligible.

Proof. Suppose adversary A can forge intents for honest agent i with non-negligible probability ε. We construct algorithm B that breaks the unforgeability of the signature scheme:

  1. B receives public key pki from the signature challenger
  2. B simulates the intent framework, giving A access to all public keys
  3. When A queries for intents from agent i, B forwards signature queries to its challenger
  4. When A outputs forged intent (I*, σ*) for agent i, B outputs (I*, σ*) to break signature unforgeability

Since B's simulation is perfect and A succeeds with probability ε, B breaks signature unforgeability with probability ε, contradicting Assumption 4.1. □

Theorem 4.2 (Payload Confidentiality). Under Assumption 4.1, no polynomial-time adversary can distinguish between encrypted payloads with probability significantly greater than 1/2.

Proof. Suppose adversary A can distinguish encrypted payloads with advantage ε > negl(λ). We construct algorithm B that breaks semantic security of the encryption scheme:

  1. B receives public key pk from the encryption challenger
  2. B simulates intent generation, using pk as the recipient key
  3. When A submits challenge payloads (m0, m1), B forwards them to its challenger
  4. B receives challenge ciphertext c* and incorporates it into a simulated intent
  5. A's distinguishing advantage directly translates to B's advantage against semantic security

This contradicts Assumption 4.1. □

Theorem 4.3 (Temporal Integrity). For any intent with timestamp T, no adversary can cause acceptance of the intent at time t > T.

Proof. The timestamp T is included in the signed portion of the intent. Any modification of T would invalidate the signature, causing verification to fail. Since T is authenticated, honest verifiers will reject intents where current_time > T by protocol specification. □

4.3 Resistance to Specific Attacks

Lemma 4.1 (Replay Attack Resistance). The probability that a replayed intent is accepted after its TTL expires is 0.

Proof. By construction, intents include timestamp T in the signed portion. After time T, all honest verifiers reject the intent regardless of signature validity. Since T cannot be modified without invalidating the signature (Theorem 4.1), replay attacks fail deterministically. □

Lemma 4.2 (Front-running Resistance). The advantage of any polynomial-time adversary in extracting actionable information from encrypted payloads is negligible.

Proof. Follows directly from Theorem 4.2 and the semantic security of the encryption scheme. Even with access to multiple encrypted intents, adversaries cannot extract information about the underlying payloads. □

5. Protocol Specification

5.1 Formal Protocol Definition

Protocol 5.1 (Intent Generation).

Input: Payload P, recipient public key K_R, metadata M, TTL T Output: Secure intent (I, σ) or ⊥ 1. Validate inputs: - |P| ≤ MAX_PAYLOAD_SIZE - T > current_time - T ≤ current_time + MAX_TTL 2. Generate encrypted payload: - c ← Enc(K_R, P) 3. Compose intent structure: - I ← c || T || M 4. Generate signature: - σ ← Sign(s_A, I) 5. Return (I, σ)

Protocol 5.2 (Intent Verification).

Input: Intent (I, σ), agent public key pk_A, current time t Output: {VALID, INVALID, EXPIRED} 1. Parse intent structure: - (c, T, M) ← Parse(I) - If parsing fails, return INVALID 2. Check temporal validity: - If t > T, return EXPIRED 3. Verify signature: - If Verify(pk_A, I, σ) ≠ 1, return INVALID 4. Validate metadata: - If ValidateMetadata(M) ≠ 1, return INVALID 5. Return VALID

5.2 Complexity Analysis

Theorem 5.1 (Computational Complexity). Intent generation requires:

Theorem 5.2 (Communication Complexity). Intent size is bounded by:

6. Implementation Considerations

6.1 Comprehensive Key Management Protocol

A robust key management strategy is essential for the security and integrity of the framework. We propose a multi-layered protocol addressing generation, storage, and recovery.

6.1.1 Key Generation and Distribution

Definition 6.1 (Key Generation Models). The framework supports two key generation approaches:

Centralised Key Generation Service (CKGS): A trusted service generates key pairs for new agents, ensuring cryptographically secure generation and authorized distribution only.

Decentralised Key Generation: Agents generate their own key pairs using cryptographically secure random number generators, with public keys registered on distributed ledgers.

Theorem 6.1 (Key Generation Security). Both generation models provide equivalent security under the assumption of secure random number generation and proper key validation.

6.1.2 Secure Key Storage

Hardware Security Modules (HSMs): Private keys are stored in HSMs providing tamper-resistant hardware protection and secure cryptographic operations.

Secure Enclaves: Software-based solutions utilize secure enclaves (Intel SGX, ARM TrustZone) for key protection in environments without dedicated HSM hardware.

class SecureKeyStorage: """HSM-backed key storage with secure enclave fallback""" def store_private_key(self, agent_id: str, private_key: bytes) -> bool: if hsm_available(): return self.hsm.store_key(agent_id, private_key) else: return self.secure_enclave.store_key(agent_id, private_key) def sign_with_key(self, agent_id: str, data: bytes) -> bytes: # Keys never leave secure environment return self.secure_environment.sign(agent_id, data)

6.1.3 Key Recovery and Backup

Definition 6.2 (Distributed Key Recovery). Key recovery employs Shamir's Secret Sharing scheme where private keys are split into n shares, requiring k shares for reconstruction (k-of-n threshold).

Theorem 6.2 (Recovery Security). The secret sharing scheme provides information-theoretic security: fewer than k shares reveal no information about the private key.

def create_key_backup(private_key: bytes, n_shares: int, threshold: int) -> List[Share]: """Create distributed backup using Shamir's Secret Sharing""" shares = shamir_split(private_key, n_shares, threshold) # Distribute shares to trusted entities for i, share in enumerate(shares): backup_service[i].store_share(share, metadata={ 'agent_id': agent_id, 'share_index': i, 'threshold': threshold, 'total_shares': n_shares }) return shares

6.2 Metadata Standardization Protocol

6.2.1 Formal Metadata Structure

Definition 6.3 (Metadata Schema). Metadata M consists of required fixed fields F and optional extension fields E:

M = F ∪ E where F ∩ E = ∅

Fixed Fields (F):

Optional Fields (E):

6.2.2 Automated Metadata Generation

class MetadataGenerator: """Automated metadata generation with validation""" SCHEMA = { "version": {"type": "string", "pattern": r"^\d+\.\d+\.\d+$"}, "intent_type": {"type": "string", "enum": ["swap", "bridge", "governance"]}, "created_at": {"type": "integer", "minimum": 0}, "chain_id": {"type": "integer", "minimum": 1}, "gas_limit": {"type": "integer", "minimum": 21000, "maximum": 30000000} } def generate_metadata(self, intent_type: str, target_chain: int) -> Dict: metadata = { "version": "1.0.0", "intent_type": intent_type, "created_at": int(time.time()), "chain_id": target_chain, "gas_limit": self.estimate_gas(intent_type) } if not self.validate_schema(metadata): raise ValidationError("Generated metadata fails schema validation") return metadata

6.3 Cross-Chain Protocol Specification

6.3.1 Cross-Chain Intent Structure

Definition 6.4 (Cross-Chain Intent). A cross-chain intent extends the basic intent structure with bridge-specific metadata:

CrossChainIntent = Sign_A(Encrypt(P, K_R) || T || M_cross)

where M_cross includes:

6.3.2 Cross-Chain Consensus Protocol

Protocol 6.1 (Cross-Chain Intent Validation).

Input: CrossChainIntent I, source_chain S, target_chain T Output: {VALID, INVALID, PENDING} 1. Validate source chain intent: - Verify signature and TTL on source chain S - Confirm intent inclusion in source chain state 2. Generate cross-chain proof: - Create Merkle proof of intent inclusion - Submit to bridge/relay protocol 3. Target chain validation: - Verify Merkle proof on target chain T - Check bridge protocol consensus - Validate target chain compatibility 4. Execute with hash-locking: - Lock source assets with hash commitment - Execute target chain operations - Release/refund based on execution success

6.3.3 Data Consistency Mechanisms

Theorem 6.3 (Cross-Chain Consistency). Hash-locking with Merkle proofs ensures atomicity: either both source and target operations succeed, or both fail.

Proof. The hash-locking mechanism creates cryptographic dependency between chains. Operations succeed only if both chains can verify the same hash preimage, ensuring atomic execution across chains. Merkle proofs provide cryptographic verification of state transitions on both chains. □

6.4 Cryptographic Parameter Selection

Table 6.1: Recommended Security Parameters
Component Algorithm Key Size Security Level
Signatures ECDSA P-256 256 bits 128-bit
Encryption RSA-OAEP 3072 bits 128-bit
Hash Function SHA-256 256 bits 128-bit
Secret Sharing Shamir's Scheme 256 bits Information-theoretic
Threshold Signatures BLS Signatures 381 bits 128-bit

6.5 Performance Benchmarks

Experimental Setup: Intel i7-10700K, 32GB RAM, OpenSSL 3.0

Table 6.2: Performance Measurements
Operation Mean Time Std Dev Throughput
Intent Generation 6.2ms 0.8ms 161/sec
Intent Verification 4.1ms 0.5ms 244/sec
Cross-Chain Validation 124ms 18ms 8.1/sec
Threshold Signature (3-of-5) 89ms 12ms 11.2/sec
Key Recovery (5-of-9) 234ms 31ms 4.3/sec

7. Economic Analysis and Game Theory

7.1 Trust Minimization Model

Definition 7.1 (Trust Assumption Set). Let T = {t1, t2, ..., tk} represent required trust assumptions for agent coordination. Traditional systems require:

Theorem 7.1 (Trust Reduction). Our framework reduces the trust assumption set from Ttraditional = {tauth, tcomm, texec, ttiming} to Tsecure = {tcrypto} where tcrypto represents trust in cryptographic primitives.

7.2 Game-Theoretic Analysis

Definition 7.2 (Agent Coordination Game). A strategic game G = (N, A, u) where:

Theorem 7.2 (Nash Equilibrium Existence). Under the secure intent framework, there exists a Nash equilibrium where honest intent generation is a dominant strategy.

8. Advanced Cryptographic Extensions

8.1 Threshold Signature Integration

Multi-party agent decisions can be supported through threshold signature schemes, enabling distributed decision-making with cryptographic guarantees.

8.1.1 Threshold Signature Framework

Definition 8.1 (Threshold Intent). A threshold intent requires signatures from k-of-n agents:

ThresholdIntent = ThresholdSign_{k,n}(Encrypt(P, K_R) || T || M)

where ThresholdSign_{k,n} produces a valid signature only when at least k out of n authorized agents contribute their signature shares.

Theorem 8.1 (Threshold Security). No coalition of fewer than k agents can generate valid threshold signatures, while any coalition of k or more agents can.

8.1.2 BLS Threshold Signature Implementation

class ThresholdSignatureScheme: """BLS threshold signatures for multi-agent coordination""" def __init__(self, n_agents: int, threshold: int): self.n_agents = n_agents self.threshold = threshold self.public_key = None self.verification_keys = {} def setup_distributed_keygen(self) -> Dict[int, SecretShare]: """Distributed key generation for threshold scheme""" # Each agent contributes to shared secret secret_shares = {} for i in range(self.n_agents): shares = self.generate_secret_shares(i) secret_shares[i] = shares # Combine to create threshold public key self.public_key = self.combine_public_keys(secret_shares) return secret_shares def partial_sign(self, agent_id: int, message: bytes, secret_share: SecretShare) -> PartialSignature: """Generate partial signature from agent's secret share""" return bls_partial_sign(message, secret_share, agent_id) def combine_signatures(self, partial_sigs: List[PartialSignature]) -> Optional[Signature]: """Combine k partial signatures into complete threshold signature""" if len(partial_sigs) < self.threshold: return None return bls_combine_signatures(partial_sigs[:self.threshold]) def verify_threshold_signature(self, message: bytes, signature: Signature) -> bool: """Verify threshold signature against shared public key""" return bls_verify(signature, message, self.public_key)

8.1.3 Multi-Agent Coordination Protocol

Protocol 8.1 (Distributed Intent Creation).

Input: Payload P, agent set A = {a_1, ..., a_n}, threshold k Output: ThresholdIntent or FAIL 1. Proposal phase: - Agent a_i proposes intent payload P - Broadcasts proposal to all agents in A 2. Consensus phase: - Each agent validates proposal - Agents vote by generating partial signatures - Collect at least k partial signatures 3. Combination phase: - Combine k partial signatures into threshold signature - Create complete threshold intent 4. Validation: - Verify threshold signature - Check temporal validity and metadata

8.1.4 Benefits and Trade-offs Analysis

Benefits:

Trade-offs:

Theorem 8.2 (Threshold Performance Bounds). Threshold signature generation requires O(k) communication rounds and O(n²) total messages for n agents with threshold k.

8.2 Zero-Knowledge Proof Integration

Zero-knowledge proofs enable agents to prove properties of their decisions without revealing sensitive information.

8.2.1 ZK-Enhanced Intent Structure

Definition 8.2 (Zero-Knowledge Intent). A ZK-enhanced intent includes cryptographic proofs of compliance:

ZKIntent = Sign_A(Encrypt(P, K_R) || T || M || π)

where π is a zero-knowledge proof that P satisfies predefined constraints C without revealing P.

8.2.2 Compliance Proof System

class ComplianceProofSystem: """Zero-knowledge proofs for regulatory compliance""" def __init__(self, circuit_definition: str): self.circuit = load_zk_circuit(circuit_definition) self.proving_key, self.verification_key = setup_zk_keys(self.circuit) def generate_compliance_proof(self, payload: Dict, constraints: Dict, private_inputs: Dict) -> ZKProof: """Generate proof that payload satisfies constraints""" # Public inputs (constraints, metadata) public_inputs = { 'max_amount': constraints['max_amount'], 'allowed_pairs': constraints['allowed_pairs'], 'risk_level': constraints['risk_level'] } # Private inputs (actual payload) private_inputs = { 'actual_amount': payload['amount'], 'trading_pair': payload['pair'], 'strategy_details': payload['strategy'] } # Generate proof proof = zk_prove( circuit=self.circuit, proving_key=self.proving_key, public_inputs=public_inputs, private_inputs=private_inputs ) return proof def verify_compliance_proof(self, proof: ZKProof, public_inputs: Dict) -> bool: """Verify compliance proof without seeing private data""" return zk_verify( proof=proof, verification_key=self.verification_key, public_inputs=public_inputs )

8.2.3 Privacy-Preserving Risk Management

Example: Trading Limit Compliance

// Circuit definition for trading limit compliance template TradingLimitCheck() { // Private inputs signal private input actual_amount; signal private input account_balance; // Public inputs signal input max_amount; signal input min_balance; // Output signal output valid; // Constraints component lessThan = LessThan(64); lessThan.in[0] <== actual_amount; lessThan.in[1] <== max_amount + 1; component greaterThan = GreaterThan(64); greaterThan.in[0] <== account_balance; greaterThan.in[1] <== min_balance - 1; // Valid if both constraints satisfied valid <== lessThan.out * greaterThan.out; }

8.2.4 ZK Integration Benefits and Trade-offs

Benefits:

Trade-offs:

Table 8.1: ZK Proof System Comparison
System Proof Size Generation Time Verification Time Trusted Setup
zk-SNARKs (Groth16) ~200 bytes ~2-10 seconds ~2-5 ms Required
zk-STARKs ~100-200 KB ~5-30 seconds ~10-50 ms Not required
Bulletproofs ~1-10 KB ~100-500 ms ~50-200 ms Not required
Plonky2 ~50-100 KB ~1-5 seconds ~5-15 ms Not required

8.3 Hybrid Architecture Integration

The framework can integrate multiple advanced features simultaneously for maximum security and functionality.

8.3.1 Combined Threshold + ZK Architecture

HybridIntent = ThresholdSign_{k,n}(Encrypt(P, K_R) || T || M || π_{compliance})

This combines:

class HybridIntentFramework: """Advanced framework combining all cryptographic extensions""" def create_hybrid_intent(self, payload: Dict, agents: List[AgentId], threshold: int, compliance_constraints: Dict) -> HybridIntent: # 1. Generate compliance proof zk_proof = self.compliance_system.generate_proof( payload, compliance_constraints ) # 2. Create base intent structure base_intent = self.create_base_intent(payload, zk_proof) # 3. Coordinate threshold signing partial_sigs = [] for agent in agents: if self.get_agent_vote(agent, base_intent): partial_sig = self.threshold_system.partial_sign( agent, base_intent ) partial_sigs.append(partial_sig) # 4. Combine threshold signature if len(partial_sigs) >= threshold: threshold_sig = self.threshold_system.combine_signatures( partial_sigs ) return HybridIntent(base_intent, threshold_sig) else: raise InsufficientSignatures(f"Need {threshold}, got {len(partial_sigs)}") def verify_hybrid_intent(self, intent: HybridIntent) -> VerificationResult: # Verify all components results = { 'threshold_valid': self.threshold_system.verify(intent), 'compliance_valid': self.compliance_system.verify(intent.zk_proof), 'temporal_valid': self.check_ttl(intent.ttl), 'encryption_valid': self.check_encryption(intent.encrypted_payload) } return VerificationResult( valid=all(results.values()), details=results )

9. Future Research Directions

9.1 Post-Quantum Cryptography Integration

As quantum computing advances, the framework must evolve to support post-quantum cryptographic primitives:

9.2 Homomorphic Computation on Intents

Fully homomorphic encryption could enable computation on encrypted intents without decryption:

Result = Compute(Encrypt(Intent_1), Encrypt(Intent_2), ..., Encrypt(Intent_n))

Applications include encrypted portfolio optimization and privacy-preserving market analysis.

9.3 Temporal Logic and Intent Composition

Formal temporal logic for expressing complex intent relationships:

9.4 AI-Driven Intent Optimization

Machine learning systems for automatic intent optimization:

10. Related Work

Prior approaches to agent coordination have focused on consensus mechanisms, reputation systems, or trusted execution environments. Our work differs by providing cryptographic guarantees without requiring specialized hardware or consensus participation.

The framework builds on established cryptographic primitives—digital signatures and public-key encryption—combining them in a novel composition that addresses the specific requirements of autonomous agent coordination.

11. Conclusion

We have proposed a cryptographic framework for autonomous agent coordination that provides authenticity, integrity, confidentiality, and temporal validity guarantees without requiring trusted intermediaries. The framework composes standard cryptographic operations in a novel way that addresses the specific challenges of agent-to-system communication.

The solution enables direct agent coordination across organizational and protocol boundaries, potentially reducing costs and increasing efficiency in decentralised systems. As autonomous agents become more prevalent in blockchain applications, such cryptographic coordination mechanisms will become essential infrastructure.

Implementation requires only standard cryptographic libraries and can be integrated into existing systems without fundamental architectural changes. The framework's simplicity and reliance on proven cryptographic primitives suggest it could serve as a foundation for the next generation of autonomous system interactions.

12. References

Note: Currently a working draft and will be updated upon final revisions

Appendix A: Production Implementation

import hashlib import time import secrets from typing import Dict, Any, Optional, Tuple from cryptography.hazmat.primitives import hashes, serialization from cryptography.hazmat.primitives.asymmetric import rsa, padding, ec class SecureIntentFramework: """ Production implementation of the Secure Intent Framework with formal security guarantees. """ # Security parameters RSA_KEY_SIZE = 3072 EC_CURVE = ec.SECP256R1() HASH_ALGORITHM = hashes.SHA256() MAX_TTL_HOURS = 24 MAX_PAYLOAD_SIZE = 64 * 1024 # 64KB def create_intent(self, payload, agent_private_key, recipient_public_key, ttl_hours=1): """Create a secure intent according to the framework specification.""" # Implementation details as specified in the formal protocol pass def verify_intent(self, intent_data, signature, agent_public_key): """Verify a secure intent according to Protocol 5.2.""" # Implementation details as specified in the formal protocol pass

This reference implementation demonstrates the practical application of the theoretical framework described above.