NovaFabric: Tamper-Evident, Replayable Evidence for Autonomous AI Agent Runs
- URL: http://arxiv.org/abs/2609.12582v1
- Date: Fri, 11 Sep 2026 08:35:43 GMT
- Title: NovaFabric: Tamper-Evident, Replayable Evidence for Autonomous AI Agent Runs
- Abstract summary: We present NovaFabric, producing audit-grade execution evidence.<n>It records an agent run, without modifying agent logic, into a portable Run Capsule.<n>Sealed runs are re-executable under a four-mode replay protocol.<n>We evaluate eight research questions at measured scope.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: When an autonomous AI agent does something consequential, what can be proven about what it did? Agent-observability platforms capture traces, but a trace is mutable: alterable undetected, with no recipe for re-executing it, silent on whether captured secrets were removed. Regulation (EU AI Act, ISO 42001, NIST AI RMF) presumes records an independent party can check. We present NovaFabric, producing audit-grade execution evidence: provider-neutral, tamper-evident, replayable, shareable. It records an agent run, without modifying agent logic, into a portable Run Capsule (fifteen-entity schema), sealed with a holistic DSSE signature, RFC 3161 timestamp, Merkle log and redaction attestation. Sealed runs are re-executable under a four-mode replay protocol and exportable as an Evidence Bundle for third-party verification with stock tooling (specified, not evaluated). The contribution is integration, not new cryptography: OpenTelemetry, DSSE/in-toto and W3C PROV. We evaluate eight research questions at measured scope. Mocked replay serves every model response from the capsule (no live model call, 10/10) but is offline w.r.t. models, not the network; only 2/10 tool-using workloads completed; the gap is missing tool-response substitution. Tampering is rejected across three tested classes. Declared-stream completeness is 0.652 (95% CI +/-0.064, ten scenarios). A repaired rule pack redacts 14/14 credential types, preserving 9/9 decoys; diff localises 140/140 mutations. Blast-radius queries: 45.5ms p99 over 10M edges (3.3x faster than a columnar baseline), 167.9ms over 100M (1 client, n=30). A 314-machine, ten-region run finds capsule REST ingest lossless but capped at 61.6 req/s (p99 26.8s) by per-worker serialisation. Six defects found in NovaFabric and its evaluation corpus: four fixed, one withdrawn, one open. Verification is conditional on a stated trusted computing base.
Related papers
- SWE-Test: Benchmarking LLM Vulnerability Discovery via Input Prediction [13.719725391379276]
Vulnerability discovery is an important ability of large language model (LLM) agents.<n>We recast its measurement as an input-prediction task with a closed, deterministic ground truth.<n>This decomposes discovery into three task modes over 22 real-world C/C++ programs spanning 15 domains.
arXiv Detail & Related papers (2026-09-05T19:04:41Z) - AI-Assisted Design of a Post-Quantum Cryptographic Accelerator: A Deployed-Silicon Case Study [0.4251773265892765]
We replace the standard acceptance gate with a byte-exact goldenreference oracle.<n>We report 232 experiments in which an agentic large language model drove a unified ML-KEM-768 and ML-DSA-65 accelerator.
arXiv Detail & Related papers (2026-09-03T16:34:50Z) - VeriPhy: Agentic Physical Reasoning for World Model Evaluation and Refinement [57.86962208273888]
We present an auditable physical-verification system in which a text-only planner compiles the prompt into typed physical obligations.<n>Each action returns a provenance-carrying evidence record whose payload, when usable, is either a typed measurement or an explicitly tagged learned state.<n>We anchor evaluation in a 1,500-clip corpus of human-annotated flaw records that localize real generation failures in prompt reference, space, and time.
arXiv Detail & Related papers (2026-09-02T20:36:45Z) - AgentProv: Auditing Agentic LLM API Providers via Tool-use Policy Probes [38.37599802008238]
Commercial LLM APIs advertise a specific foundation model, but the served backbone may be silently substituted, quantized, or wrapped.<n>All existing audits decide backbone identity from the text-output channel.<n>We introduce Agentic Provenance (AgentProv), the first action-based identity audit for agentic LLM APIs.
arXiv Detail & Related papers (2026-08-30T08:22:12Z) - Recognition Without Enforcement: Configuration-Dependent Failures in LLM Agent Instruction Arbitration and External Control [0.0]
LLM agents arbitrate among instructions from system prompts, users, memory, and tools, but this arbitration cannot be assumed to enforce trust boundaries.<n>We identify a recognition-enforcement gap: source-format features are linearly decodable from model activations, and models can explicitly identify forged authority when prompted.<n>We treat model self-arbitration as a capability rather than a security boundary and implement an external reference monitor combining authenticated source routing with capability-gated tool execution.
arXiv Detail & Related papers (2026-08-28T16:34:05Z) - The Recall Trap: A Recall-Maximizing Retriever Configuration Reduces Issue Resolution in Fixed-Budget Code Context [45.88028371034407]
We report a controlled case study in code repair, not a new phenomenon.<n>We toggle one flag (one-chunk-perfile deduplication) on an otherwise identical stack.<n>We map where it holds: it reverses on a lexical BM25 (3.2pp, significant cross-paradigm interaction)<n>We show that at a tight fixed budget, do not hard-deduplicate by file, and A/B packing policies against the task, not the metric flag was tuned.
arXiv Detail & Related papers (2026-08-14T19:22:50Z) - Looping Is Not Reliability: State-Bound Evidence and Typed Revision Contracts for Agentic Code Repair [36.56438114281786]
We study the gap between finding a correct patch and retaining, verifying, and submitting it.<n>We derive an evidence-bound typed loop contract and instantiate its mechanically enforceable subset in a reference implementation.
arXiv Detail & Related papers (2026-07-27T16:05:23Z) - AgentDebugX: An Open-Source Toolkit for Failure Observability, Attribution, and Recovery in LLM Agents [83.37969790806069]
LLM agent failures are difficult to debug because the step where an error surfaces is often not the one that caused it.<n>We present Agent DebuggerX, an open-source framework that organizes debug as a closed loop of Detect, Attribute, Recover, and Rerun.<n>At its core, Deep Debugger performs multi-turn root-cause diagnosis through global trajectory understanding.
arXiv Detail & Related papers (2026-07-21T06:21:13Z) - Fantastic Adaptive Taxonomies and How to Use Them [100.20614173157708]
AdaMAST converts execution traces into a compact, evidence-grounded failure taxonomy.<n>No code is hand-authored, no trace human-annotated.<n>In agent-system search, taxonomy-coded diagnoses of failed candidates outperform free-form.
arXiv Detail & Related papers (2026-07-17T17:41:16Z) - Auto: The AGI Compiler [0.0]
Auto is a compiler that records live agent behavior, measures which parts are secretly deterministic, extracts them into verified programs or distilled specialists, and emits cognition binaries.<n>We use "AGI compiler" in one narrow, testable sense: a system that autonomously converts novel experience into permanent, verified, near-free skill while measuring what it does not know.
arXiv Detail & Related papers (2026-07-05T23:09:24Z) - Overeager Coding Agents: Measuring Out-of-Scope Actions on Benign Tasks [40.270213696031625]
OverEager-Gen is a benchmark dedicated to overeager behavior on benign tasks.<n>On Claude Code, stripping the consent declaration alone raises the overeager rate from 0.0% to 17.1%.<n>OverEager-Gen therefore certifies each scenario's discriminative power before admission.
arXiv Detail & Related papers (2026-05-18T16:00:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.