PACE: Policy-Attested Contract Execution for Safe AI Agents in Decentralized Finance
- URL: http://arxiv.org/abs/2608.17220v1
- Date: Tue, 18 Aug 2026 00:22:46 GMT
- Title: PACE: Policy-Attested Contract Execution for Safe AI Agents in Decentralized Finance
- Abstract summary: We present PACE, a transaction-level authorization framework that interposes between an LLM-based agent and on-chain execution.<n> PACE cryptographically bind the approved intent, policy, and simulation report to the exact execution bytes, with replay and expiration protection.<n> PACE achieves a 0.00 unsafe execution rate and 0.00 false-positive rate on benign tasks, compared to 0.80 for the unguarded baseline.
- Score: 9.22772901752634
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Autonomous AI agents are emerging as interfaces for decentralized finance (DeFi) actions such as swaps, lending operations, and yield management. Because these agents rely on large language models (LLMs) to plan transactions, they inherit the LLM's susceptibility to prompt injection and lack of mechanisms to bind a verifier's approval to the exact transaction ultimately submitted on-chain. We present PACE (Policy-Attested Contract Execution), a transaction-level authorization framework that interposes between an LLM-based agent and on-chain execution. PACE introduces typed transaction intents, a deterministic policy verifier, and signed Policy Decision Records (PDRs) that cryptographically bind the approved intent, policy, and simulation report to the exact execution bytes, with replay and expiration protection. A Solidity smart account enforces PDR signatures on-chain with a measured overhead of 29,826-31,822 gas. We evaluate PACE against six baselines on 40 tasks spanning four attack categories plus benign utility (2,800 trials, 10 seeds). In our deterministic sandbox, PACE achieves a 0.00 unsafe execution rate and 0.00 false-positive rate on benign tasks, compared to 0.80 for the unguarded baseline. Ablation studies identify permissive policy settings (+57.5 pp) and the touched-contract allowlist (+12.5 pp) as the dominant safety components. To test whether the same deterministic floor holds for real model outputs, the artifact additionally provides a three-model live-LLM evaluation over the full task suite with repeated runs. A mainnet-fork harness is included for archive-RPC deployments, but fork results are reported only when the corresponding artifacts are generated. These auxiliary studies are separate from, and never substitute for, the deterministic benchmark. We frame our claims as logic-level safety within a reproducible benchmark rather than deployment-ready DeFi security.
Related papers
- EvoSafeHarness: Evolving Model- and Domain-Specific Harnesses for Securing Agents [74.54929751174289]
EvoSafeHarness is a safety-specific optimization framework that synthesizes a deployable harness for a frozen model in a target domain.<n>It reduces average attack success rate from 45.6% to 10.0% at a 3.3-point utility cost.<n>It also achieves the best score on Agent-SafetyBench for every victim.
arXiv Detail & Related papers (2026-09-05T05:56:41Z) - Zero-Knowledge Predicate Proofs Between AI Agents: A Measured, Cross-Protocol Gateway and the Source-Integrity Gap [0.0]
Multi-agent AI platforms move quickly from staging to production, but the way agents establish trust remains rudimentary.<n>We take provable data minimisation between agents from proposal to running system.<n>A 32-bit threshold predicate proves in 6.2 ms and verifies in 1.0 ms with a 608-byte Bulletproofs proof.
arXiv Detail & Related papers (2026-08-30T23:16:17Z) - LMSM: LLM Security Framework Inspired by Linux Security Modules [56.93644694627783]
Large language models (LLMs) are increasingly deployed with layered defenses, yet malicious prompts can still bypass them.<n>We present Language Model Security Modules (LMSM), a security framework that adapts the separation behind Linux Security Modules (LSM) to LLM serving.
arXiv Detail & Related papers (2026-08-26T12:13:05Z) - PhyAgentOS: A Self-Evolving Operating System for Embodied Agents with Decoupled Cognitive Planning and Physical Execution [49.776611937968]
We present PhyAgentOS, a foundation delivering scheduling, verification, memory, benchmarking, and safety as system-level services.<n>Its Session-Centered treats a session, not an action, as the minimum unit of scheduling, compatibility, supervised execution, evidence collection, and acceptance.<n>SessionVerifier distinguishes execution termination from semantic task completion via evidence-grounded verdicts of success, failure, or replan.<n> Benchmarking reuses the deployment session and verification path, so results trace to real execution.
arXiv Detail & Related papers (2026-07-18T04:46:53Z) - Semantic Quorum Assurance: Collective Certification for Non-Deterministic AI Infrastructure [2.124730017640531]
We introduce Quorum Semantic Assurance (SQA), a control-plane primitive for governing non-deterministic agentic infrastructure.<n>SQA represents proposals as declarative execution contracts bound to cryptographic evidence chains and routes them to a diverse panel of read-only, sandboxed validator agents.<n>On 500 infrastructure-inspired mutation scenarios, with safety results reported on held-out safe/unsafe trials excluding ambiguous scenarios, SQA reduces unsafe approval from 18.5% for single-agent validation to 0.3%.
arXiv Detail & Related papers (2026-06-06T07:31:04Z) - GoAT-X: A Graph of Auditing Thoughts for Securing Token Transactions in Cross-Chain Contracts [52.51342355102833]
Cross-chain bridges, the critical infrastructure of the multi-chain ecosystem, have become a primary target for attackers.<n>Existing defenses, such as bytecode-level static analysis, are ill-equipped to handle the semantic complexity of cross-chain interactions.<n>We propose GoAT-X, a framework that shifts automated cross-chain smart contracts auditing from pattern matching toward systematic first-principles verification.
arXiv Detail & Related papers (2026-04-27T11:34:21Z) - Sovereign Agentic Loops: Decoupling AI Reasoning from Execution in Real-World Systems [2.124730017640531]
Sovereign Agentic Loops (SAL) is a control-plane architecture in which models emit structured intents with justifications.<n>SAL combines an obfuscation membrane, which limits model access to identity-sensitive state, with a cryptographically linked Evidence Chain for auditability and replay.<n>In an OpenKedge prototype for cloud infrastructure, SAL blocks 93% of unsafe intents at the policy layer, rejects the remaining 7% via consistency checks, and adds 12.4 ms median latency.
arXiv Detail & Related papers (2026-04-24T00:56:55Z) - Compiling Activation Steering into Weights via Null-Space Constraints for Stealthy Backdoors [48.881343993730844]
Safety-aligned large language models (LLMs) are increasingly deployed in real-world pipelines.<n> adversaries can distribute backdoored checkpoints that behave normally under standard evaluation but jailbreak when a hidden trigger is present.<n>Recent post-hoc weight-editing methods offer an efficient approach to injecting such backdoors.
arXiv Detail & Related papers (2026-04-14T06:48:33Z) - APEX: Agent Payment Execution with Policy for Autonomous Agent API Access [0.0]
APEX is a complete research system that adapts 402-style payment gating to UPI-like fiat.<n>It preserves policy-governed spend control, tokenized access verification, and replay resistance.<n>Results show that policy enforcement reduces total spending by 27.3% while maintaining 52.8% success rate for legitimate requests.
arXiv Detail & Related papers (2026-04-02T13:32:01Z) - Before the Tool Call: Deterministic Pre-Action Authorization for Autonomous AI Agents [0.0]
Open Agent Passport (OAP) is an open specification and reference implementation that intercepts tool calls synchronously before execution.<n>OAP enforces authorization decisions in a measured median of 53 ms (N=1,000)<n>The same infrastructure that enforces security constraints also enforces quality gates, operational contracts, and compliance controls.
arXiv Detail & Related papers (2026-03-21T21:34:09Z) - ReasAlign: Reasoning Enhanced Safety Alignment against Prompt Injection Attack [52.17935054046577]
We present ReasAlign, a model-level solution to improve safety alignment against indirect prompt injection attacks.<n>ReasAlign incorporates structured reasoning steps to analyze user queries, detect conflicting instructions, and preserve the continuity of the user's intended tasks.
arXiv Detail & Related papers (2026-01-15T08:23:38Z) - Safe and Compliant Cross-Market Trade Execution via Constrained RL and Zero-Knowledge Audits [0.5586191108738564]
We present a cross-market algorithmic trading system that balances execution quality with rigorous compliance enforcement.<n>The architecture comprises a high-level planner, a reinforcement learning execution agent, and an independent compliance agent.<n>We report effects at the 95% confidence level using paired t-tests and examine tail risk via CVaR.
arXiv Detail & Related papers (2025-10-06T15:52:12Z) - AegisLLM: Scaling Agentic Systems for Self-Reflective Defense in LLM Security [74.22452069013289]
AegisLLM is a cooperative multi-agent defense against adversarial attacks and information leakage.<n>We show that scaling agentic reasoning system at test-time substantially enhances robustness without compromising model utility.<n> Comprehensive evaluations across key threat scenarios, including unlearning and jailbreaking, demonstrate the effectiveness of AegisLLM.
arXiv Detail & Related papers (2025-04-29T17:36:05Z)
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.