ENCRUST: Encapsulated Substitution and Agentic Refinement on a Live Scaffold for Safe C-to-Rust Translation

• URL: http://arxiv.org/abs/2604.04527v1
• Date: Mon, 06 Apr 2026 08:46:14 GMT
• Title: ENCRUST: Encapsulated Substitution and Agentic Refinement on a Live Scaffold for Safe C-to-Rust Translation
• Abstract summary: We present Encapsulated Substitution and Agentic Refinement on a Live Scaffold for Safe C-to-construct Translation.<n>We present a two-phase pipeline for translating real-world C projects to safe Rust.<n>Phase 1 (Encapsulated Substitution) translates each function using an ABIutil wrapper that splits it into two components.<n>Phase 2 (Agentic Refinement) resolves unsafe constructs beyond per-function scope.
• Score: 5.10072982044534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Encapsulated Substitution and Agentic Refinement on a Live Scaffold for Safe C-to-Rust Translation, a two-phase pipeline for translating real-world C projects to safe Rust. Existing approaches either produce unsafe output without memory-safety guarantees or translate functions in isolation, failing to detect cross-unit type mismatches or handle unsafe constructs requiring whole-program reasoning. Furthermore, function-level LLM pipelines require coordinated caller updates when type signatures change, while project-scale systems often fail to produce compilable output under real-world dependency complexity. Encrust addresses these limitations by decoupling boundary adaptation from function logic via an Application Binary Interface (ABI)-preserving wrapper pattern and validating each intermediate state against the integrated codebase. Phase 1 (Encapsulated Substitution) translates each function using an ABI-preserving wrapper that splits it into two components: a caller-transparent shim retaining the original raw-pointer signature, and a safe inner function targeted by the LLM with a clean, scope-limited prompt. This enables independent per-function type changes with automatic rollback on failure, without coordinated caller updates. A deterministic, type-directed wrapper elimination pass then removes wrappers after successful translation. Phase 2 (Agentic Refinement) resolves unsafe constructs beyond per-function scope, including static mut globals, skipped wrapper pairs, and failed translations, using an LLM agent operating on the whole codebase under a baseline-aware verification gate. We evaluate Encrust on 7 GNU Coreutils programs and 8 libraries from the Laertes benchmark, showing substantial unsafe-construct reduction across all 15 programs while maintaining full test-vector correctness.

Related papers

• 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)
• From C to Idiomatic Rust: A Ship-of-Theseus Agentic Translation [0.13537117504260618]
  C underpins operating systems, embedded platforms, and network infrastructure because its abstractions map directly to machine behaviour.<n> Rust eliminates most of failure modes through a static ownership and borrowing model that enforces memory safety and aliasing constraints at compile time.<n>This paper presents a migration methodology that first generates a semantics-preserving, non-idiomatic Rust baseline and then incrementally rewrites it into idiomatic Rust using agentic AI.
  arXiv  Detail & Related papers  (2026-07-30T21:00:27Z)
• VCT: A Verifiable Transcript System for LLM Conversations [5.3181399750078535]
  Verifiable Conversation Transcript builds non-linear LLM semantic operations into account-level authenticated state transitions.<n>Security analysis demonstrates that, under standard cryptographic assumptions, VCT guarantees the integrity, consistency, verifiable shareability, and non-repudiation of account-level conversation records.
  arXiv  Detail & Related papers  (2026-06-22T08:17:39Z)
• Securing LLM Agents Need Intent-to-Execution Integrity [49.490963596514185]
  We argue that securing LLM agents requires defining an end-to-end correctness property that specifies when an agent's execution faithfully reflects the user's intent.<n>We observe that LLM agents are structurally analogous to compilers, where security violations correspond to mis-executions that do not preserve user intent.<n>We derive four integrity properties that must hold simultaneously: emphTool Integrity, emphInstruction Integrity, emphJudgment Integrity, and emphData Flow Integrity.
  arXiv  Detail & Related papers  (2026-05-16T12:53:31Z)
• 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)
• From Inference Routing to Agent Orchestration: Declarative Policy Compilation with Cross-Layer Verification [10.651054435747385]
  The Semantic Router DSL is a non-Turing-complete policy language deployed in production for per-request inference.<n>This paper extends the same language from stateless, per-request routing to multi-step agent routing.
  arXiv  Detail & Related papers  (2026-03-28T15:04:31Z)
• RealSec-bench: A Benchmark for Evaluating Secure Code Generation in Real-World Repositories [58.32028251925354]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, but their proficiency in producing secure code remains a critical, under-explored area.<n>We introduce RealSec-bench, a new benchmark for secure code generation meticulously constructed from real-world, high-risk Java repositories.
  arXiv  Detail & Related papers  (2026-01-30T08:29:01Z)
• Qwen3Guard Technical Report [127.69960525219051]
  We present Qwen3Guard, a series of multilingual safety guardrail models with two specialized variants.<n>Generative Qwen3Guard casts safety classification as an instruction-following task to enable fine-grained tri-class judgments.<n>Stream Qwen3Guard introduces a token-level classification head for real-time safety monitoring.
  arXiv  Detail & Related papers  (2025-10-16T04:00:18Z)
• Large Language Model-Powered Agent for C to Rust Code Translation [2.182572303351317]
  A modern system programming language, Rust, has emerged as a memory-safe alternative to the C programming language.<n>Applying the agentic capability for the C-to-Rust translation introduces distinct challenges.<n>Unlike math or commonsense QA, the intermediate steps required for C-to-Rust are not well-defined.<n>We propose a novel intermediate step, the Virtual Fuzzing-based equivalence Test (VFT), and an agentic planning framework, the LLM-powered Agent for C-to-Rust code translation (LAC2R)
  arXiv  Detail & Related papers  (2025-05-21T01:26:23Z)
• CRUST-Bench: A Comprehensive Benchmark for C-to-safe-Rust Transpilation [51.18863297461463]
  CRUST-Bench is a dataset of 100 C repositories, each paired with manually-written interfaces in safe Rust as well as test cases.<n>We evaluate state-of-the-art large language models (LLMs) on this task and find that safe and idiomatic Rust generation is still a challenging problem.<n>The best performing model, OpenAI o1, is able to solve only 15 tasks in a single-shot setting.
  arXiv  Detail & Related papers  (2025-04-21T17:33:33Z)
• ReF Decompile: Relabeling and Function Call Enhanced Decompile [50.86228893636785]
  The goal of decompilation is to convert compiled low-level code (e.g., assembly code) back into high-level programming languages.<n>This task supports various reverse engineering applications, such as vulnerability identification, malware analysis, and legacy software migration.
  arXiv  Detail & Related papers  (2025-02-17T12:38:57Z)
• Syzygy: Dual Code-Test C to (safe) Rust Translation using LLMs and Dynamic Analysis [8.361424157571468]
  Syzygy is an automated approach to translate C to safe Rust.<n>This is the largest automated and test-validated C to safe Rust code translation achieved so far.
  arXiv  Detail & Related papers  (2024-12-18T18:55:46Z)

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.