599 lines
33 KiB
Plaintext
599 lines
33 KiB
Plaintext
Internet-Draft AI/Agent WG
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Intended status: standards-track March 2026
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Expires: September 02, 2026
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Agent Behavior Verification Protocol (ABVP) for Runtime Compliance Validation
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draft-ai-agent-behavior-verification-protocol-00
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Abstract
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This document defines the Agent Behavior Verification Protocol
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(ABVP), a standardized framework for continuously validating that
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deployed AI agents operate according to their declared policies
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and specifications. As autonomous agents become increasingly
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prevalent in critical systems, there is a growing gap between
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stated agent capabilities and actual runtime behavior
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verification. ABVP provides mechanisms for real-time behavior
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monitoring, policy compliance validation, and cryptographic
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attestation of agent actions against predefined behavioral
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specifications. The protocol defines a verification architecture
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that includes behavior witnesses, compliance checkers, and
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attestation chains to ensure agents maintain fidelity to their
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declared operational parameters. ABVP integrates with existing
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agent accountability frameworks while providing specific
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mechanisms for runtime verification, behavioral drift detection,
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and compliance reporting. This specification addresses the
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critical need for trustworthy agent deployment by enabling
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operators to continuously verify agent behavior matches stated
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policies throughout the agent lifecycle.
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Status of This Memo
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This Internet-Draft is submitted in full conformance with the
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provisions of BCP 78 and BCP 79.
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This document is intended to have standards-track status.
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Distribution of this memo is unlimited.
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Table of Contents
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1. Introduction ................................................ 3
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2. Terminology ................................................. 4
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3. Problem Statement ........................................... 5
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4. Agent Behavior Verification Architecture .................... 6
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5. Behavior Specification Format ............................... 7
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6. Runtime Verification Protocol ............................... 8
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7. Compliance Reporting and Attestation ........................ 9
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8. Security Considerations ..................................... 10
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9. IANA Considerations ......................................... 11
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1. Introduction
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The proliferation of autonomous AI agents in critical
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infrastructure, financial systems, and decision-making processes
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has created an urgent need for continuous verification that these
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agents operate according to their declared policies and behavioral
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specifications. Traditional approaches to agent deployment rely on
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pre-deployment testing and static policy validation, which fail to
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address the dynamic nature of agent behavior in production
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environments. As agents adapt, learn, and respond to changing
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conditions, their actual runtime behavior may diverge
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significantly from their original specifications, creating
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security vulnerabilities, compliance violations, and operational
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risks that remain undetected until system failures occur.
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Existing agent accountability frameworks primarily focus on post-
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hoc analysis and audit trails, providing limited capability for
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real-time behavior verification and immediate detection of policy
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violations. This reactive approach is insufficient for autonomous
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systems that make critical decisions with limited human oversight,
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where behavioral drift or policy violations can have immediate and
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severe consequences. Current verification methodologies also lack
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standardized protocols for expressing behavioral constraints in
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machine-verifiable formats, making it difficult to establish
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consistent compliance validation across diverse agent
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implementations and deployment environments.
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The gap between declared agent capabilities and actual runtime
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behavior verification represents a fundamental trust problem in
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autonomous systems deployment. Organizations deploying AI agents
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face significant challenges in ensuring that agents continue to
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operate within specified parameters throughout their operational
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lifecycle, particularly as agents encounter novel situations not
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covered in initial testing scenarios. This verification gap
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undermines confidence in agent reliability and limits the adoption
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of autonomous systems in high-stakes environments where behavioral
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compliance is critical for safety, security, and regulatory
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compliance.
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The Agent Behavior Verification Protocol (ABVP) addresses these
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challenges by providing a standardized framework for continuous
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runtime verification of agent behavior against declared
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specifications. ABVP enables real-time monitoring of agent
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actions, automated compliance checking against behavioral
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policies, and cryptographic attestation of verification results to
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establish trust chains for agent operation validation. The
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protocol is designed to integrate with existing agent
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architectures while providing mechanisms for detecting behavioral
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drift, validating policy adherence, and generating verifiable
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evidence of agent compliance throughout the operational lifecycle.
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This specification defines the core protocol mechanisms, message
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formats, and verification procedures necessary to implement
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trustworthy agent behavior validation in production deployments.
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2. Terminology
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The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL
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NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL"
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in this document are to be interpreted as described in RFC 2119
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[RFC2119].
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Agent: An autonomous software entity that performs actions or
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makes decisions according to defined policies and specifications.
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In the context of ABVP, an agent is a system whose runtime
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behavior requires continuous verification against its declared
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operational parameters and behavioral constraints.
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Behavior Specification: A formally defined set of policies,
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constraints, and operational parameters that describe the expected
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and permitted actions of an agent. A behavior specification MUST
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be machine-readable and verifiable, containing sufficient detail
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to enable automated compliance checking during agent runtime
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operation.
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Behavior Witness: A system component or external entity that
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observes and records agent actions for verification purposes. A
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behavior witness MUST provide cryptographically signed
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attestations of observed agent behavior and MAY operate
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independently of the agent being monitored to ensure verification
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integrity.
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Compliance Validation: The process of evaluating agent runtime
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behavior against its declared behavior specification to determine
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conformance. Compliance validation encompasses real-time
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monitoring, policy checking, and the generation of verification
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results that attest to agent adherence to specified behavioral
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constraints.
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Verification Attestation: A cryptographically signed statement
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that asserts the compliance status of an agent's behavior relative
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to its specification during a defined time period. Verification
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attestations MUST include sufficient detail to enable third-party
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validation and SHOULD reference the specific behavior
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specification version and verification criteria used in the
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assessment.
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Behavioral Drift: The phenomenon where an agent's actual runtime
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behavior gradually diverges from its declared specification over
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time, either due to learning adaptations, environmental changes,
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or system degradation. ABVP mechanisms MUST be capable of
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detecting behavioral drift and reporting deviations from
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established behavioral baselines.
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3. Problem Statement
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The proliferation of autonomous AI agents in critical
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infrastructure, financial systems, and safety-critical
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applications has created an urgent need for continuous
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verification that deployed agents operate within their declared
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behavioral boundaries. Current agent deployment practices rely
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primarily on pre-deployment testing and static policy
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declarations, creating a significant verification gap between an
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agent's stated capabilities and constraints and its actual runtime
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behavior. This gap becomes particularly problematic as agents
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adapt their behavior through learning mechanisms, interact with
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dynamic environments, or experience gradual behavioral drift due
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to model degradation or adversarial influences.
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Traditional software verification approaches are insufficient for
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autonomous agents because agent behavior is often non-
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deterministic, context-dependent, and may evolve over time through
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machine learning processes. Unlike conventional software systems
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where behavior can be predicted from code analysis, agent systems
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exhibit emergent behaviors that arise from complex interactions
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between training data, environmental inputs, and decision-making
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algorithms. The absence of standardized mechanisms for expressing
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machine-verifiable behavioral specifications further complicates
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runtime verification, as operators lack a common framework for
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defining what constitutes compliant agent behavior and how
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compliance can be automatically validated.
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The security and trust implications of unverified agent behavior
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are substantial, particularly in scenarios where agents operate
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with elevated privileges or make decisions affecting human safety
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or economic systems. Behavioral drift, where an agent's actions
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gradually deviate from intended policies, may go undetected for
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extended periods without continuous verification mechanisms.
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Similarly, adversarial attacks that subtly modify agent behavior
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to achieve malicious objectives could remain unnoticed in systems
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that lack real-time compliance monitoring. The inability to
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provide cryptographic attestations of agent behavior compliance
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also prevents the establishment of trust chains necessary for
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multi-agent systems or cross-organizational agent interactions.
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Current accountability frameworks for AI systems focus primarily
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on explainability and audit trails but do not provide mechanisms
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for real-time verification of behavioral compliance against
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formally specified policies. This creates operational risks where
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agents may violate their declared constraints without immediate
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detection, potentially causing system failures, security breaches,
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or regulatory violations. The lack of standardized verification
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protocols also prevents interoperability between different agent
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verification systems and limits the ability to establish industry-
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wide trust frameworks for autonomous agent deployment.
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4. Agent Behavior Verification Architecture
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The ABVP architecture consists of four primary components that
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work together to provide continuous runtime verification of agent
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behavior: Agent Runtime Environments (AREs), Behavior Verification
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Nodes (BVNs), Attestation Authorities (AAs), and Verification
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Clients (VCs). Agent Runtime Environments host the deployed agents
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and MUST implement behavior monitoring capabilities that capture
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relevant behavioral data and forward it to designated Behavior
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Verification Nodes. These environments MUST provide secure
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isolation between the agent execution context and the monitoring
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subsystem to prevent agents from interfering with their own
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verification processes. The ARE MUST also implement a trusted
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communication channel to BVNs using protocols such as TLS 1.3
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[RFC8446] or QUIC [RFC9000] to ensure behavior data integrity
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during transmission.
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Behavior Verification Nodes serve as the core verification engines
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within the ABVP architecture and MUST implement the runtime
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verification protocol defined in Section 6. Each BVN maintains a
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repository of behavior specifications for agents under its
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verification authority and continuously processes behavioral
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evidence received from AREs. BVNs MUST validate incoming behavior
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data against the appropriate specifications and generate
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compliance assessments in real-time. Multiple BVNs MAY collaborate
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in a distributed verification network to provide redundancy and
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prevent single points of failure. When operating in a distributed
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configuration, BVNs MUST implement consensus mechanisms to ensure
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consistent verification results across the network. BVNs MUST also
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implement rate limiting and resource management to handle high-
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volume verification requests without compromising verification
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quality.
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Attestation Authorities provide cryptographic attestation services
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for verified behavior compliance and MUST maintain secure key
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management infrastructure capable of generating unforgeable
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attestations. AAs receive compliance reports from BVNs and MUST
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verify the authenticity and integrity of these reports before
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issuing attestations. The AA MUST implement a hierarchical trust
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model where attestations can be validated through a chain of trust
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extending to a root certificate authority. AAs SHOULD implement
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hardware security modules (HSMs) or equivalent trusted execution
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environments to protect attestation signing keys from compromise.
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Multiple AAs MAY participate in cross-attestation relationships to
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provide attestation redundancy and prevent single points of trust
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failure.
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Verification Clients represent entities that consume ABVP
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attestations to make trust decisions about agent behavior and MAY
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include system operators, regulatory bodies, or other automated
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systems. VCs MUST implement attestation verification capabilities
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including certificate chain validation and revocation checking as
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specified in Section 7. The architecture MUST support both real-
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time verification queries and batch verification processes to
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accommodate different operational requirements. VCs SHOULD
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implement local attestation caching with appropriate cache
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invalidation mechanisms to reduce verification latency while
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maintaining attestation freshness. The ABVP architecture MUST
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provide clear separation of duties between verification components
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to prevent conflicts of interest and ensure independent
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verification processes.
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The communication between architectural components MUST follow the
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protocol specifications defined in Section 6, with all inter-
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component communications authenticated and encrypted. The
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architecture MUST support both synchronous and asynchronous
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verification modes to accommodate different agent deployment
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scenarios and performance requirements. Components MUST implement
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appropriate logging and audit trail capabilities to support
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forensic analysis and compliance reporting. The overall
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architecture SHOULD be designed for horizontal scalability to
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support large-scale agent deployments while maintaining
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verification performance and reliability.
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5. Behavior Specification Format
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This section defines the standardized format for expressing agent
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behavioral policies and constraints within the ABVP framework. The
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behavior specification format enables machine-readable policy
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declarations that can be automatically verified during agent
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runtime. All behavior specifications MUST be expressed in a
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structured format that supports both human readability and
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automated processing by verification systems.
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The core behavior specification is structured as a JSON document
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conforming to the ABVP Behavior Schema. Each specification MUST
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contain a policy declaration section, verification parameters, and
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compliance thresholds. The policy declaration section includes
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behavioral constraints expressed as logical predicates, allowed
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action sets, and resource utilization bounds. Verification
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parameters specify the monitoring frequency, sampling rates, and
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attestation requirements for each declared behavior. Compliance
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thresholds define the acceptable deviation ranges and tolerance
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levels for measured behaviors compared to declared specifications.
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Behavioral constraints within the specification are expressed
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using a formal constraint language based on temporal logic
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predicates. Each constraint MUST specify a behavioral property
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(such as "response_time_bound" or "resource_utilization_limit"),
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an operator (such as "less_than", "equals", or "within_range"),
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and target values or ranges. Complex behavioral policies MAY be
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constructed using logical operators (AND, OR, NOT) to combine
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multiple constraints. The specification format supports
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hierarchical constraint groupings to represent different
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operational modes or contextual behavior variations.
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The behavior specification includes a verification requirements
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section that defines how each behavioral constraint should be
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monitored and validated. This section MUST specify the required
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verification frequency, acceptable measurement methods, and
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cryptographic attestation parameters for each constraint.
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Verification requirements MAY include sampling strategies for
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performance-sensitive constraints and continuous monitoring
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directives for safety-critical behaviors. The specification format
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also supports conditional verification rules that adjust
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monitoring parameters based on agent operational context or
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detected behavioral patterns.
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Each behavior specification MUST include metadata sections
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containing versioning information, validity periods, and
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specification dependencies. The metadata enables proper
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specification lifecycle management and ensures compatibility
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between agent deployments and verification infrastructure.
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Specifications SHOULD include digital signatures from authorized
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policy authors to ensure specification integrity and authenticity.
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The format supports specification inheritance and composition,
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allowing complex agent policies to be built from validated
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behavioral specification components while maintaining verification
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traceability throughout the composition hierarchy.
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6. Runtime Verification Protocol
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The Runtime Verification Protocol defines the message exchange
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patterns and procedures that enable continuous monitoring and
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validation of agent behavior against declared specifications. The
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protocol operates on a request-response model where Verification
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Requesters initiate compliance checks, Behavior Monitors observe
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agent actions, and Compliance Checkers evaluate adherence to
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behavioral specifications. All protocol participants MUST
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implement the core verification message set defined in this
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section, and MAY implement optional extensions for specialized
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verification scenarios. The protocol is designed to operate over
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existing transport mechanisms including HTTP/2 [RFC7540],
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WebSocket [RFC6455], or dedicated secure channels established
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through TLS 1.3 [RFC8446].
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Verification sessions are initiated through a VERIFICATION_REQUEST
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message that specifies the agent identifier, behavioral
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specification reference, verification scope, and temporal
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parameters for the compliance check. The requesting entity MUST
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include a cryptographically secure session identifier, timestamp
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bounds for the verification window, and references to the specific
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behavioral constraints to be validated. Behavior Monitors respond
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with MONITORING_DATA messages containing timestamped observations
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of agent actions, decision traces, and relevant contextual
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information captured during the specified verification window.
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These messages MUST include integrity protection through digital
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signatures and SHOULD include privacy-preserving mechanisms when
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agent actions contain sensitive information.
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Compliance evaluation proceeds through COMPLIANCE_CHECK messages
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exchanged between Verification Requesters and designated
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Compliance Checkers. Each compliance check message MUST reference
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the behavioral specification being evaluated, include the
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monitoring data to be assessed, and specify the verification
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algorithms or rules to be applied. Compliance Checkers process the
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monitoring data against the behavioral constraints and generate
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COMPLIANCE_RESULT messages indicating whether the observed
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behavior satisfies the specified requirements. Results MUST
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include binary compliance indicators, detailed violation reports
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when non-compliance is detected, and confidence metrics indicating
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the reliability of the compliance assessment.
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The protocol includes mechanisms for handling streaming
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verification scenarios where agent behavior must be validated
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continuously rather than in discrete sessions. Streaming
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verification employs persistent connections where MONITORING_DATA
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messages are transmitted in near real-time as agent actions occur,
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enabling immediate detection of behavioral deviations. Compliance
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Checkers maintain running assessments of behavioral compliance and
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generate COMPLIANCE_ALERT messages when violations are detected or
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when behavioral patterns indicate potential drift from specified
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policies. All streaming verification sessions MUST implement flow
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control mechanisms to prevent resource exhaustion and SHOULD
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include adaptive sampling techniques to manage verification
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overhead in high-throughput scenarios.
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Attestation generation occurs through ATTESTATION_REQUEST messages
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that trigger the creation of cryptographic proofs of compliance
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assessment results. These requests MUST specify the compliance
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results to be attested, the cryptographic algorithms to be used
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for attestation generation, and any additional claims or
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assertions to be included in the attestation. The resulting
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ATTESTATION_RESPONSE messages contain digitally signed
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attestations that bind compliance results to specific agents, time
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periods, and behavioral specifications through tamper-evident
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cryptographic structures. Attestations MUST include sufficient
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information to enable independent verification of compliance
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claims and SHOULD reference the complete verification audit trail
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to support forensic analysis when behavioral violations occur.
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7. Compliance Reporting and Attestation
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Compliance reporting in ABVP provides a standardized mechanism for
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documenting and cryptographically attesting to agent behavior
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verification results. A compliance report MUST contain the agent
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identifier, verification period, evaluated behavior
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specifications, compliance status for each specification, and
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supporting evidence including behavioral observations and
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verification computations. Reports MUST be generated at
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configurable intervals or upon detection of compliance violations,
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with emergency reports triggered immediately when critical policy
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violations occur. The reporting format MUST support both human-
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readable summaries and machine-processable structured data to
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enable automated compliance monitoring and audit trail generation.
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Cryptographic attestation ensures the integrity and non-
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repudiation of compliance reports through digital signatures and
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hash chain mechanisms. Each compliance report MUST be digitally
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signed by the generating Compliance Checker using keys certified
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within the ABVP trust framework. Attestations MUST include a
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timestamp from a trusted time source, the hash of the previous
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attestation to form a verification chain, and sufficient
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cryptographic binding to prevent tampering or replay attacks. The
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attestation format SHOULD follow established standards such as RFC
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8392 (CWT) or RFC 7519 (JWT) to ensure interoperability with
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existing security infrastructures.
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Trust chain establishment requires a hierarchical certification
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authority structure where Compliance Checkers obtain certificates
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from trusted ABVP Certificate Authorities. Root certificates for
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ABVP trust anchors MUST be distributed through secure channels and
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updated using standard certificate management practices as defined
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in RFC 5280. Verification entities MUST validate the complete
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certificate chain from the signing Compliance Checker to a trusted
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root before accepting attestations. Certificate revocation MUST be
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supported through standard mechanisms such as Certificate
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Revocation Lists (CRLs) or Online Certificate Status Protocol
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(OCSP) as specified in RFC 5280 and RFC 6960 respectively.
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The compliance reporting protocol defines specific message formats
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for distributing attestations to interested parties including
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agent operators, regulatory authorities, and other verification
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systems. Compliance reports MAY be distributed through push
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mechanisms to subscribed entities or pulled on-demand through
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standardized query interfaces. Report distribution MUST preserve
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attestation integrity while allowing for appropriate access
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control based on the sensitivity of the reported agent behaviors.
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Long-term storage and archival of compliance reports SHOULD
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implement tamper-evident logging mechanisms to support forensic
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analysis and regulatory compliance requirements.
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8. Security Considerations
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The ABVP verification infrastructure introduces several security
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considerations that must be addressed to ensure the integrity and
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trustworthiness of agent behavior verification. The protocol's
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reliance on continuous monitoring and attestation creates
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potential attack vectors that could compromise the verification
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process itself. Attackers may attempt to subvert verification
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mechanisms to mask non-compliant agent behavior or to falsely
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indicate compliance violations where none exist. The verification
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system MUST be designed with the assumption that both the
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monitored agents and the verification infrastructure may be
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targets of sophisticated adversaries seeking to undermine
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behavioral compliance validation.
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Attestation integrity represents a critical security requirement
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for ABVP implementations. Verification attestations MUST be
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cryptographically signed using mechanisms that provide non-
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repudiation and tamper detection capabilities. The attestation
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chain MUST be anchored in a trusted root of trust, such as
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hardware security modules or trusted platform modules, to prevent
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forgery of compliance attestations. Implementations SHOULD employ
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time-stamping mechanisms to prevent replay attacks where old
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attestations are reused to mask current non-compliance. The
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cryptographic algorithms used for attestation signing MUST conform
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to current best practices for digital signatures and SHOULD
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support algorithm agility to enable updates as cryptographic
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standards evolve. Key management for attestation signing MUST
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follow established security practices, including regular key
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rotation and secure key storage.
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The distributed nature of ABVP verification creates additional
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security challenges related to verification node compromise and
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Byzantine behavior among verification participants. Verification
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nodes may be compromised by attackers seeking to manipulate
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compliance reporting or inject false verification results.
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Implementations MUST employ consensus mechanisms or threshold-
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based verification approaches to detect and mitigate the impact of
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compromised verification nodes. The protocol SHOULD include
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mechanisms for verification node authentication and authorization
|
|
to prevent unauthorized participants from joining verification
|
|
networks. Network communications between verification components
|
|
MUST be encrypted and authenticated to prevent eavesdropping and
|
|
man-in-the-middle attacks. Implementations SHOULD implement rate
|
|
limiting and anomaly detection to identify potential denial-of-
|
|
service attacks against verification infrastructure.
|
|
|
|
Behavioral specification tampering and specification substitution
|
|
attacks pose significant threats to the ABVP framework's
|
|
effectiveness. Attackers may attempt to modify behavioral
|
|
specifications to make non-compliant behavior appear compliant or
|
|
to introduce specifications that are impossible to verify
|
|
accurately. Behavioral specifications MUST be cryptographically
|
|
protected through digital signatures and integrity checking
|
|
mechanisms. The protocol MUST include versioning and change
|
|
tracking for behavioral specifications to detect unauthorized
|
|
modifications. Verification systems SHOULD implement specification
|
|
validation to detect specifications that contain logical
|
|
inconsistencies or verification bypasses. Access controls for
|
|
specification modification MUST follow principle of least
|
|
privilege and include audit logging of all specification changes.
|
|
|
|
The ABVP verification process may inadvertently expose sensitive
|
|
information about agent operations, internal state, or the systems
|
|
being monitored. Verification data collection MUST be designed to
|
|
minimize information disclosure while maintaining verification
|
|
effectiveness. Implementations SHOULD employ privacy-preserving
|
|
techniques such as zero-knowledge proofs or selective disclosure
|
|
mechanisms where appropriate to limit exposure of sensitive
|
|
operational details. Verification logs and attestations MUST be
|
|
protected against unauthorized access and SHOULD include data
|
|
retention policies that balance verification auditability with
|
|
privacy requirements. The protocol MUST consider the implications
|
|
of cross-border data flows when verification infrastructure spans
|
|
multiple jurisdictions with different privacy regulations.
|
|
|
|
Side-channel attacks and covert channels represent additional
|
|
security considerations for ABVP implementations. The verification
|
|
process itself may create observable patterns that could be
|
|
exploited by attackers to infer information about agent behavior
|
|
or verification outcomes. Timing-based side channels in
|
|
verification operations MAY reveal information about the
|
|
complexity or results of compliance checking. Implementations
|
|
SHOULD consider countermeasures such as constant-time operations
|
|
and traffic analysis resistance where appropriate. The protocol
|
|
design MUST consider how verification metadata and communication
|
|
patterns might be used to build profiles of agent behavior that
|
|
could compromise operational security or reveal sensitive system
|
|
characteristics.
|
|
|
|
9. IANA Considerations
|
|
|
|
This document requires the registration of several new namespaces
|
|
and protocol parameters with the Internet Assigned Numbers
|
|
Authority (IANA). These registrations are necessary to ensure
|
|
global uniqueness and interoperability of ABVP implementations
|
|
across different vendors and deployment environments.
|
|
|
|
IANA SHALL establish a new registry titled "Agent Behavior
|
|
Verification Protocol (ABVP) Parameters" under the "Structured
|
|
Syntax Suffixes" registry group. This registry SHALL contain three
|
|
sub-registries: "Behavior Specification Schema Types",
|
|
"Verification Message Types", and "Attestation Format
|
|
Identifiers". The registration policy for all ABVP parameter sub-
|
|
registries SHALL follow the "Specification Required" policy as
|
|
defined in RFC 8126, with the additional requirement that all
|
|
registrations include a reference to a publicly available
|
|
specification document and demonstrate interoperability with at
|
|
least one existing ABVP implementation.
|
|
|
|
The "Behavior Specification Schema Types" sub-registry SHALL
|
|
maintain identifiers for standardized behavior specification
|
|
formats as defined in Section 5. Each registration MUST include a
|
|
unique identifier string, a human-readable description, a
|
|
reference specification, and version information. Initial
|
|
registrations SHALL include "abvp-policy-v1" for the base policy
|
|
specification format and "abvp-constraints-v1" for behavioral
|
|
constraint specifications. The "Verification Message Types" sub-
|
|
registry SHALL contain identifiers for protocol messages defined
|
|
in Section 6, including verification requests, compliance reports,
|
|
and attestation messages. Registration entries MUST specify the
|
|
message identifier, purpose, required parameters, and applicable
|
|
verification contexts.
|
|
|
|
The "Attestation Format Identifiers" sub-registry SHALL maintain
|
|
identifiers for cryptographic attestation formats used in
|
|
compliance reporting as specified in Section 7. Each registration
|
|
MUST include the attestation format identifier, cryptographic
|
|
algorithm requirements, trust model specifications, and
|
|
interoperability considerations. IANA SHALL reserve the identifier
|
|
prefix "abvp-" for protocol-specific attestation formats and MAY
|
|
delegate sub-namespace management to recognized standards bodies
|
|
for domain-specific attestation requirements. All registry entries
|
|
MUST include contact information for the registrant and SHALL be
|
|
subject to periodic review to ensure continued relevance and
|
|
security adequacy.
|
|
|
|
Author's Address
|
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|
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Generated by IETF Draft Analyzer
|
|
2026-03-01
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