File-Level Context Enhances LLM Robustness Against Adversarial Feedback Loops

Abstract

Abstract: This report synthesises findings from 7 peer-reviewed papers addressing the following research question: To what extent does access to file-level context improve the robustness of DeepSeek-R1 and Claude against adversarial feedback loops in the FeedbackEval benchmark. As Large Language Models (LLMs) become increasingly integrated into secure software development workflows, a critical question remains unanswered: can these models not only detect insecure code but also reliably classify vulnerabilities according to standardized taxonomies? In. 8 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.4/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

To what extent does access to file-level context improve the robustness of DeepSeek-R1 and Claude against adversarial feedback loops in the FeedbackEval benchmark?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance