arXiv AI
Jul 13
The Patchwork Problem in LLM-Generated Code
arXiv:2607. 08981v1 Announce Type: cross Abstract: LLM-generated code often compiles, passes tests, and appears correct, yet breaks once deployed.
Measuring LLM Trust Allocation Across Conflicting Software Artifacts
arXiv:2604. 03447v2 Announce Type: replace-cross Abstract: LLM-based software engineering assistants often reason over multiple artifacts, including code, documentation, signatures, and tests, even when those artifacts are incomplete or mutually inconsistent.
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arXiv AI
4d ago
SINGED: Correct Outputs Do Not Certify Safe Execution in LLM Agents
arXiv:2609.35889v1 Announce Type: cross Abstract: Tool-using language-model agents select and execute third-party artifacts. Different implementations can return the requested output while producing...
arXiv AI
Jul 10
From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents
arXiv:2607. 08028v1 Announce Type: new Abstract: Enterprise large language model (LLM) applications often begin as prototypes whose behavior is carried by prompts and retrieval context.
arXiv AI
Jul 8
Beyond Refusal: A Same-Lineage Study of Aligned and Abliterated LLMs for Vulnerability Analysis
arXiv:2607. 05842v1 Announce Type: cross Abstract: Large language model (LLM)-assisted software security operates at a difficult boundary: the vulnerability-analysis terminology needed for legitimate code review, triage, and repair can closely resemble terminology associated with misuse.
arXiv Machine Learning
Sep 3
CodePoisonRAG: Knowledge Poisoning Attacks on Retrieval-Augmented Code Generation
The paper introduces CodePoisonRAG, a framework that poisons retrieval-augmented code generation systems by transforming benign code artifacts into malicious ones. It injects CWE-specific vulnerabilities and false safety claims into a single task-matched artifact, achieving high success rates across multiple generators and even against a defense system. The study demonstrates that attackers can target and propagate specific weaknesses without altering the underlying language model.
arXiv AI
2d ago
Detecting Inconsistencies in Model Specifications with LLM-as-Verifier Reasoning
arXiv:2610.01847v1 Announce Type: cross Abstract: Model specifications define how large language models (LLMs) should behave, guiding alignment training, inference-time behavior, and evaluation. Yet...
arXiv Machine Learning
Jul 1
An Empirical Study of Security Calibration in Large Language Models for Code
arXiv:2606. 31159v1 Announce Type: cross Abstract: Large Language Models (LLMs) are rapidly transforming software development, yet their use in security-critical contexts raises a key question: do models know when their generated code is insecure?
arXiv AI
Sep 15
TyPatch: Transforming Patches into Typestate Rules for Kernel Bug Detection
arXiv:2609.13728v1 Announce Type: cross Abstract: Historical Linux kernel patches capture defect knowledge that applies beyond their original repair sites. Recent work has shown that large language m...
arXiv AI
Jun 4
From Agent Traces to Trust: Evidence Tracing and Execution Provenance in LLM Agents
arXiv:2606. 04990v1 Announce Type: cross Abstract: Large language model (LLM)-based agents increasingly solve complex tasks by interacting with external tools, retrieval systems, memory modules, environments, and other agents.
arXiv AI
Sep 25
SWE-Prometheus: Measuring Engineering Governance Improvements in Real-World Repositories
SWE-Prometheus is a new benchmark that evaluates large language model coding agents on the broader task of improving repository engineering governance, rather than just fixing individual issues. It presents fixed snapshots with open-ended objectives, requiring agents to identify risks, prioritize interventions, and verify changes across six governance dimensions. The benchmark includes 60 repositories and reports metrics such as Normalized Governance Improvement and behavior‑breakage rates, providing a nuanced view of how different models affect governance artifacts and execution‑backed improvements.
arXiv AI
Jul 3
An Exploratory Study on LLM-Generated Code and Comments in Code Repositories
arXiv:2607. 01867v1 Announce Type: cross Abstract: The use of LLMs in software development has become increasingly widespread on tasks such as code generation and summarization.
arXiv Computation and Language
Sep 15
Fabrication After Tool Failure: Tool-Augmented Agents Assert Values Their Tools Did Not Return
arXiv:2609.14758v1 Announce Type: cross Abstract: Tool-augmented language models are evaluated on whether they reach the right answer, not on whether they report honestly when a tool fails to supply...