Offline-Verifiable Accountability for Cross-Organization Agent Messaging: A Preserved Evidence-Bundle Approach
Authors: Adil Alshammari, Hayretdin Bahsi
Organizations: School of Informatics, Computing, and Cyber Systems, Northern Arizona University, United States · Department of Computer Science, College of Computer and Information Sciences, Majmaah University, Majmaah 11952, Saudi Arabia · School of Information Technologies, Tallinn University of Technology, Estonia
Cross-organization agent workflows require preserved evidence that remains independently verifiable during later audit or dispute review. They may involve multiple organizations, delegated actions, policy-relevant events, and disputed accountability claims. This is difficult when live systems are unavailable, controlled by one party, or not trusted by all participants. Existing mechanisms provide useful pieces, including authenticated logging, delegation semantics, signed checkpoints, and consistency checks. What remains missing is a verifier-centered event-level bundle for checking evidence sufficiency offline under an explicit policy. We propose a preserved evidence-bundle model and a policy-controlled offline verifier for agent-to-agent workflow events. Each bundle preserves policy-required evidence, including sender authentication, authenticated log commitment, witness-backed checkpoint evidence, append-only continuity, delegation-aware authorization evidence, and explicit receiver-signed receipt evidence when required. The verifier accepts only claims supported by the selected policy-required evidence, giving a later reviewer an offline basis for assessing evidence sufficiency. It does not infer delivery or receipt from transport behavior or log inclusion alone. In a prototype evaluation over 300 complete workflows and 1200 valid preserved bundles, we measure offline verifier-side latency across policy profiles and workflow-event evidence requirements. Checkpoint-context anchoring has the highest latency in the current prototype, while delegation and workflow-prerequisite evidence require additional verification steps. In targeted negative-evidence tests, all corrupted or policy-insufficient bundles were rejected, with no false acceptance observed. These results support evidence-based audit and dispute review without relying on live services or platform-specific logs.
Figures & tables
Reference
Year
Context
Evidence bundle
Offline verification
Signed checkpoint
Inclusion proof
Witness / consistency
Delegation evidence
Dirksen et al. [ 8 ]
2021
Web PKI
×
△
△
△
✓
×
Malvai et al. [ 21 ]
2023
E2EE messaging
×
△
△
△
✓
×
Li et al. [ 19 ]
2024
Medical data
×
△
×
×
×
✓
Ferretti et al. [ 11 ]
2021
IIoT
×
△
△
△
×
✓
This work
2026
Agent messaging
✓
✓
✓
✓
✓
✓
Table 1: Comparison of related work by verifier-relevant evidence support.
Boundary
Verifier implication
Agent-side domains
Verify sender identity and policy-bound evidence.
Untrusted transport
Do not treat transport success as evidence.
Evidence-producing services
Check only policy-required evidence.
Preserved evidence stores
Check for omission, substitution, or tampering.
Local verifier trust base
Use local policy, keys, and trust anchors.
External action boundary
Do not infer endpoint correctness or execution.
Table 2: Evidence-control boundaries and verifier implications.
Notation
Description
m
Canonicalized message representation.
h(m)
Message commitment derived from m .
H(⋅)
Hash or commitment function.
σS
Sender signature over the canonical message.
σR
Explicit receiver-signed receipt.
σL
Log signature over signed-checkpoint fields.
Table 3: Main notation.
Artifact
Supports
Does not establish
Sender signature
Message authorship
Logging, receipt, or delivery
Log evidence
Log commitment
Receipt, delivery, or endpoint correctness
Signed receipt
Receipt claim
Delivery or downstream execution
Witness evidence
Checkpoint support
Receipt, delivery, or real-time equivocation prevention
AOEP evidence
Append-only extension
Global consistency or collusion resistance
Checkpoint context
Checkpoint anchoring
Continuity, receipt, or endpoint correctness
Table 4: Evidence artifacts and claim limits.
Figure 1: Sender-authenticated messages are validated, logged, augmented with policy-required checkpoint evidence, and preserved as evidence bundles for offline verification.
Figure 2: Sequence-level evidence generation for the running workflow example. Event 1 is refer_patient , Event 2 is order_mri , Event 3 is send_result , and Event 4 is submit_claim . Events 1–3 share the signed-message, validation, logging, and evidence-storage pattern. Event 4 adds delegation and workflow-prerequisite evidence.
Figure 3: Offline verification inputs and policy-controlled checks over a preserved evidence bundle.
Figure 4: Policy-controlled evidence checks used by the fully enabled verifier profile and the single-control policy comparison.
ID
Property
Required evidence and scope
P1
Authenticated log commitment
Inclusion proof and signed checkpoint; verified checkpoint only.
P2
Sender-authenticated message binding
Sender-authenticated message fields and payload-to-log binding evidence.
P3
Append-only continuity
Prior-checkpoint context and append-only extension evidence; policy-enabled.
P4
Witness-backed checkpoint validity
Witness evidence; does not imply receipt, delivery, or real-time equivocation prevention.
P5
Capability and policy-required workflow checks
Delegation-aware authorization evidence and required workflow evidence.
P6
Checkpoint-context validation
Checkpoint-context evidence and permitted WCL context; policy-enabled.
Table 5: Verifier-supported security properties and required evidence.
ID
Security threat
Affected evidence
Evidence issue
Props.
T1
Identity spoofing
Signed-message submission
False or impersonated sender identity.
P2
T2
Log tampering
Log evidence
Inclusion proof, signed checkpoint, or log material modified.
P1
T3
Rebinding
Payload-to-log binding
Commitment, leaf index, or Merkle path modified or rebound.
Capability or workflow evidence forged, rebound, or missing.
P5
Table 6: Evidence-level security threats mapped to verifier-supported properties.
Exp.
Evaluation question
Method
Interpretation
E1
Which policy checks add verifier time?
Single-control comparison over valid submit_claim bundles.
Latency attribution relative to the ALL_ON baseline.
E2
Does latency vary by event evidence?
Paired comparison across the four workflow events.
Event-level evidence analysis, not throughput benchmarking.
E3
Does invalid or policy-insufficient evidence fail at the expected check?
Targeted negative-evidence cases mapped to checks, threats, and properties.
Diagnostic rejection coverage, not exhaustive adversarial search.
Table 7: Evaluation design and claim-matched purpose of each experiment.
Profile
Disabled verifier control
Median [95% CI] (ms)
P95 (ms)
Reduction vs. ALL_ON
ALL_ON
None
785.52 [774.38–799.62]
1064.33
–
NO_WIT
Witness validation
745.60 [738.85–755.89]
954.92
5.1%
NO_AOEP
AOEP continuity
761.93 [754.90–772.95]
1063.04
3.0%
NO_CAP
Capability/workflow check
761.82 [751.83–775.11]
1027.94
3.0%
NO_EPOCH
Checkpoint-context anchoring
183.04 [174.52–200.43]
417.64
76.7%
Table 8: Experiment E1 within-experiment verifier-side latency attribution under single-control policy comparisons for valid submit_claim bundles ( n=300 per profile).
Figure 5: Offline verification latency across workflow event types under the fully enabled verifier profile ( n=300 paired workflows). Circles show median latency with 95% bootstrap confidence intervals, square markers show P95 latency, and vertical spans show the median-to-P95 range. The y-axis uses a truncated lower bound for readability. The submit_claim event requires the most verification time because it exercises the richest workflow-accountability path.
Case
Verifier check
Threat ID
Property
Mutation or misuse
C1
Payload binding
T3
P2
Canonical payload modified after issuance.
C2
Merkle inclusion
T3
P1
Audit-path sibling hash replaced.
C3
Merkle inclusion
T3
P1
Leaf index modified while preserving the bundle structure.
C4
Signed checkpoint validation
T2
P1
Signed checkpoint root modified.
C5
Append-only continuity
T5
P3
AOEP proof element modified between checkpoints.
C6
Capability binding
T6
P5
Capability scope modified.
Table 9: Negative-evidence cases mapped to evidence-level security threats and verifier-supported properties.
Long-horizon agents execute thousands of actions, resulting in sequential failures rather than isolated errors. When a coding agent deletes a production database or a prompt injection spreads across agents, the incident raises questions of causality, authority, and non-repudiable third-party verification. The Agent Flight Recorder captures each agent action as a structured, canonically serialized event binding eight semantic fields from intent through execution to provenance. Hash chaining and Merkle batching provide tamper evidence and compact inclusion proofs. For cross-organizational disputes where no party's infrastructure qualifies as neutral ground, periodic on-chain anchoring of epoch roots lets any verifier with the disclosed payload and Merkle proof check the record independently, without pre-agreeing on a trusted intermediary. The on-chain footprint is minimal: each anchor stores a 32-byte epoch root and a back-pointer, and no event content touches the chain. We evaluate the system across five cumulative ablation configurations on synthetic agent workloads. The full system adds ~48 microseconds median per-event latency and 512 bytes per event. L2 anchoring costs $2.30 per 100K events at 100-event epochs. The full integrity stack detects edit, delete, reorder, and fork tampering at 100% with zero false positives. Structured forensic queries achieve 1.0 precision on guardrail and delegation lookups where unstructured text search yields 0.013 and 0.077 respectively.
Laurent Bindschaedler, Quentin Botha, Christoph Siebenbrunner
Max Planck Institute for Software Systems · Research Institute for Cryptoeconomics, Vienna University of Economics and Business
Agent evaluations face two distinct evidentiary questions: whether a reported claim is recomputable from retained evidence (sufficiency), and whether the retained records cover the committed experiment set (coverage). Generic logs and hash-linked transcripts answer neither reliably. We introduce ClaimReceipt, a claim-relative receipt specification and selective verifier that binds typed transaction evidence to a signed experiment manifest and returns PASS, INVALID, or INCONCLUSIVE per claim. We freeze the specification before implementation (SHA-256 18d109...b81). On 1,392 historical buyer--seller records, a CR-2 verifier reproduces all five manually labeled audit verdicts, exactly replays 600 deterministic and 792 post-generation records, makes every one of 13 declared field groups non-redundant under tested ablations, and returns the expected result on 11/11 semantic faults with 0/8 false positives. We then run a separate prospective CR-3 epoch: 30 assignments are committed before inference, terminal receipts are signed and chained, and private evidence is encrypted for an auditor. Complete evidence yields coverage and accounting PASS; withholding one terminal receipt returns INCONCLUSIVE_COVERAGE, while withholding all private openings preserves coverage and protocol verification but makes economic claims inconclusive, exactly matching a preregistered prediction. Receipt instrumentation adds 0.021% of model-inference time and 9.9 KB per transaction. A specification-legibility probe indicates that our own frozen specification is not yet unambiguous to an independent reader. Claim verification therefore requires both claim-sufficient evidence and a committed universe against which omissions become visible.
Long-horizon, semi-verifiable agentic tasks often rely on rubric-based evaluation, but the evidence needed to assess these tasks can be spread across the environment state and the execution trace. A judge may overlook this evidence or accept unsupported claims, including an agent's own reports of success. When rubric verdicts serve as reinforcement-learning rewards, such errors can reinforce undesirable behavior. We introduce Sauron, an evidence-first agentic verifier that investigates both state and trace and relies on a claim only after independently validating it against its source. Bounded queries and joint investigation of related criteria reduce context use and redundant work, and each verdict carries an auditable receipt of its supporting evidence. Across 2,586 human-labeled criteria from 100 personal-assistant episodes, Sauron achieves the highest F1 of five verifiers on all eight backbones tested while costing less than Gandalf, the strongest baseline. With GPT-5.6 Sol, it reaches 0.96 F1 versus Gandalf's 0.92 at 23% lower cost, with a wider margin on process criteria. On a paired fault-injection suite covering 13 families, several of which simulate reward hacking, Sauron detects 97% of injected failures while flagging only 2% of clean controls, outperforming baselines even when they are given the full trace. On TRAIL, a benchmark for detecting and localizing real agent errors, Sauron leads both Gandalf and TRAIL's own judge on all three quality metrics.