The paper introduces Adaptive Unlearning (AU), a post-deployment framework that surgically suppresses code-related hallucinations, significantly reducing the risk of package confusion attacks like slo…

The paper introduces Neutral Prompting Attacks (NPA), a stealthy method showing that semantically benign prompts can covertly increase package hallucination in coding agents, creating new software sup…

The paper introduces a validated, consensus-labeled prompt bank that separates requests for executable malicious code (weapons) from requests for general harmful security knowledge, providing a more g…

The paper analyzes GitHub security advisories for LLM-integrated open-source systems, finding that while most vulnerabilities map to existing code-level weaknesses, the architectural risks like Supply…

This study empirically measures the consistency and success rate of autonomous LLM penetration testing across multiple services, finding statistically significant differences in exploitation capabilit…

This study empirically measures the consistency and effectiveness of autonomous LLM penetration testing across multiple services, finding statistically significant differences in exploitation rates am…

The paper analyzes a large dataset of JavaScript packages to demonstrate that a small number of vulnerable dependencies can propagate vulnerabilities across a disproportionately large number of packag…

The paper introduces a high-precision APT malware attribution method that uses ranked binary classifiers with explicit abstention, significantly improving accuracy when encountering unknown or out-of-…

The paper argues that the near-term impact of LLM-assisted vulnerability discovery is not simply an increase in zero-day volume, but a critical bottleneck in defender remediation throughput, shifting…

The paper introduces GuardPhish, a large-scale dataset and evaluation framework, demonstrating that even high-performing open-source LLMs can generate actionable phishing content despite accurate inte…

The paper proposes an attestation-aware promotion gate to mitigate supply-chain risks in LLM pipelines by cryptographically verifying and enforcing claims about training and release artifacts before d…

This paper systematically analyzes the threat posed by malicious third-party API routers in the LLM supply chain, finding that a significant number of routers actively perform payload injection, crede…

This paper demonstrates that by applying systematic prompting and retrieval techniques, local open-weight LLMs can significantly enhance their capabilities to autonomously perform Linux privilege esca…

This paper replicates and extends a study on Java security API misuse in LLMs, finding that while newer models improve performance, the misuse risk persists and is significantly mitigated by external…

The paper introduces the first byte-native Large Language Model (LLM) capable of analyzing raw executable binary data, achieving high accuracy in tasks like malware and architecture classification.

The paper evaluates frontier LLMs on cybersecurity tasks using dual-mode benchmarks and concludes that general-purpose models are insufficient, advocating for specialized, vertical foundation models.

The paper empirically evaluates the security quality of LLM-generated code across various prompting methods, finding that while prompting alters the structure of weaknesses, it is insufficient to reli…

The paper establishes a standardized security assessment framework and develops a multi-layered defensive system, demonstrating that systematic testing and external defenses are crucial for safe LLM d…

The paper demonstrates that advanced capabilities, such as jailbreaking large language models and finding software vulnerabilities, can be achieved effectively at zero cost by coordinating multiple sm…

The paper introduces a Hybrid Utility Minimum Bayes Risk (HUMBR) framework to significantly reduce hallucinations in high-stakes enterprise AI workflows, outperforming standard consistency methods.