arXiv subjects

Jinnan Guo

Publications and source records attributed to Jinnan Guo.

3 recordsLinked to original sources

arXiv2026

Securing Filesystems for Confidential Computing

Confidential computing protects applications inside Trusted Execution Environments (TEEs), but it leaves storage vulnerable. Even with disk encryption, a malicious cloud provider can roll back, replay, fork, or tamper with disk state, breaking the integrity and freshness guarantees required by stateful applications. Existing solutions either assume trusted storage, incur high overheads, or push integrity logic into applications. We present ShieldFS, a POSIX-compliant filesystem that provides end-to-end integrity and freshness for persistent storage in the confidential-computing threat model without requiring application changes. ShieldFS represents permissible filesystem states using succinct cryptographic commitments, maintained inside TEEs and replicated in a lightweight trusted registry. On-disk data structures, including a write-ahead log and a storage pool, are authenticated using hash chains and an embedded Merkle tree. ShieldFS utilizes transactions and copy-on-write to update persistent filesystem state and commitments atomically. The commitments are verified during reads, ensuring that rollback, replay, and equivocation attacks are detected even when the entire I/O stack is untrusted. We implement the design by extending ZFS, yielding ShieldZFS. Evaluation with standard filesystem benchmarks and real-world workloads shows that ShieldZFS provides strong integrity and freshness guarantees with performance comparable to state-of-the-art filesystems.

cs.CR↗

arXiv2026

AOSpec: Action and Observation Co-Speculation for Low-Latency Agent Serving

Large language model agents increasingly act through stateful tools, yet model generation and environment execution remain serialized at every step. As decoding accelerates, tool execution becomes a growing bottleneck. Existing action- or observation-only speculation leaves much of this latency exposed: value is concentrated in a few slow calls, some outcomes emerge only through execution, and longer lookahead typically requires an increasingly unlikely chain of action predictions. We present AOSpec, a lossless framework that co-speculates actions and observations across the full agent-environment loop. Expected Value Decoding (EVD) directs observation speculation toward outcomes with the greatest expected latency benefit, optimizing expected time hidden rather than hit rate. For outcomes only execution can reveal, AOSpec launches latency-critical target actions in isolated forks that contain their effects, while Joint Action-State Verification (JASV) verifies both the action and its origin state against committed execution before reuse. JASV recasts long-horizon action dependency from full-chain prediction into target action-state verification, breaking the lookahead--accuracy tradeoff and unlocking long-range overlap without sacrificing serial semantics. Across Terminal-Bench serving settings spanning four harnesses, five actor models, and five serving speeds, AOSpec outperforms every practical baseline, reducing mean end-to-end latency by 11.8-32.5% and p99 latency by up to 42.8%. Its gains increase as decoding accelerates, and its observation model transfers from Terminal-Bench to SWE-bench Verified without retraining.

cs.LG↗

arXiv2024

VerifiableFL: Verifiable Claims for Federated Learning using Exclaves

In federated learning (FL), data providers jointly train a machine learning model without sharing their training data. This makes it challenging to provide verifiable claims about the trained FL model, e.g., related to the employed training data, any data sanitization, or the correct training algorithm-a malicious data provider can simply deviate from the correct training protocol without detection. While prior FL training systems have explored the use of trusted execution environments (TEEs) to protect the training computation, such approaches rely on the confidentiality and integrity of TEEs. The confidentiality guarantees of TEEs, however, have been shown to be vulnerable to a wide range of attacks, such as side-channel attacks. We describe VerifiableFL, a system for training FL models that establishes verifiable claims about trained FL models with the help of fine-grained runtime attestation proofs. Since these runtime attestation proofs only require integrity protection, VerifiableFL generates them using the new abstraction of exclaves. Exclaves are integrity-only execution environments, which do not contain software-managed secrets and thus are immune to data leakage attacks. VerifiableFL uses exclaves to attest individual data transformations during FL training without relying on confidentiality guarantees. The runtime attestation proofs then form an attested dataflow graph of the entire FL model training computation. The graph is checked by an auditor to ensure that the trained FL model satisfies its claims, such as the use of data sanitization by data providers or correct aggregation by the model provider. VerifiableFL extends NVFlare FL framework to use exclaves. We show that VerifiableFL introduces less than 12% overhead compared to unprotected FL training.

cs.CR↗