On 183 validated V8/SpiderMonkey vulnerabilities, the best coding agents verify at most 38.8% of instances, and crash-only grading would inflate judged successes by 43.6%.
Steenhoek et al
6 Pith papers cite this work, alongside 17 external citations. Polarity classification is still indexing.
6
Pith papers citing it
17
external citations · external index
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
ReasonVul deploys three LLM agents with independent analysis and structured debate to achieve 40% PairAcc and 72.52% F1 on PrimeVul, outperforming baselines by 81% in PairAcc.
Veritas detects out-of-bounds vulnerabilities in stripped binaries at 90% recall by grounding LLM reasoning in static witness-backed flows and runtime validation.
SAGE uses sparse autoencoders to boost vulnerability signals in LLMs, raising internal SNR 12.7x and delivering up to 318% MCC gains on vulnerability detection benchmarks.
Fine-tuned decoder-only LLMs fall into a Semantic Trap on vulnerability detection, achieving high scores on unpaired normal code but failing on paired vulnerable-patched code, semantic perturbations, and gap analysis, while reasoning supervision reduces symptoms at the cost of recall.
Fine-tuning codegen-mono on 500 synthetic-plus-reviewed Python examples produces 99% accuracy, 98.08% precision, 100% recall and 99.04% F1 for CWE detection.
citing papers explorer
-
SEC-bench Pro: Can Language Models Solve Long-Horizon Software Security Tasks?
On 183 validated V8/SpiderMonkey vulnerabilities, the best coding agents verify at most 38.8% of instances, and crash-only grading would inflate judged successes by 43.6%.
-
Three Heads Are Better Than One: A Multi-perspective Reasoning Framework for Enhanced Vulnerability Detection
ReasonVul deploys three LLM agents with independent analysis and structured debate to achieve 40% PairAcc and 72.52% F1 on PrimeVul, outperforming baselines by 81% in PairAcc.
-
Veritas: Grounding LLM Agents for Reliable Vulnerability Reasoning over Stripped Binaries
Veritas detects out-of-bounds vulnerabilities in stripped binaries at 90% recall by grounding LLM reasoning in static witness-backed flows and runtime validation.
-
SAGE: Signal-Amplified Guided Embeddings for LLM-based Vulnerability Detection
SAGE uses sparse autoencoders to boost vulnerability signals in LLMs, raising internal SNR 12.7x and delivering up to 318% MCC gains on vulnerability detection benchmarks.
-
Do Fine-Tuned LLMs Understand Vulnerabilities? An Investigation into the Semantic Trap
Fine-tuned decoder-only LLMs fall into a Semantic Trap on vulnerability detection, achieving high scores on unpaired normal code but failing on paired vulnerable-patched code, semantic perturbations, and gap analysis, while reasoning supervision reduces symptoms at the cost of recall.
-
Case Study: Fine-tuning Small Language Models for Accurate and Private CWE Detection in Python Code
Fine-tuning codegen-mono on 500 synthetic-plus-reviewed Python examples produces 99% accuracy, 98.08% precision, 100% recall and 99.04% F1 for CWE detection.