REVIEW 8 cited by
Large Language Models in Cybersecurity: State-of-the-Art
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The rise of Large Language Models (LLMs) has revolutionized our comprehension of intelligence bringing us closer to Artificial Intelligence. Since their introduction, researchers have actively explored the applications of LLMs across diverse fields, significantly elevating capabilities. Cybersecurity, traditionally resistant to data-driven solutions and slow to embrace machine learning, stands out as a domain. This study examines the existing literature, providing a thorough characterization of both defensive and adversarial applications of LLMs within the realm of cybersecurity. Our review not only surveys and categorizes the current landscape but also identifies critical research gaps. By evaluating both offensive and defensive applications, we aim to provide a holistic understanding of the potential risks and opportunities associated with LLM-driven cybersecurity.
Forward citations
Cited by 8 Pith papers
-
ELFuzz: Efficient Input Generation via LLM-driven Synthesis Over Fuzzer Space
ELFuzz automatically evolves LLM-written input generators for large programs, outperforming grammar-based fuzzers in coverage and bug finding on seven benchmarks.
-
Scale-free congestion clusters in large-scale traffic networks: a continuum modeling study
The Aw–Rascle–Zhang continuum model on directed lattice networks yields power-law spatiotemporal congestion clusters with finite-size scaling by linear system size.
-
How Good LLM-Generated Password Policies Are?
LLM-generated pwquality.conf password policies are frequently inconsistent, hallucinated, and incorrect, so they require validation before deployment in Linux PAM systems.
-
Toward Cybersecurity-Expert Small Language Models
A family of 4B–20B cybersecurity models fine-tuned on an enriched, expert-steered reasoning dataset matches or beats larger frontier models on core CTI benchmarks.
-
Llama-3.1-FoundationAI-SecurityLLM-8B-Instruct Technical Report
Foundation-Sec-8B-Instruct, an instruction-tuned 8B cybersecurity LLM, is released and claimed to beat Llama 3.1-8B-Instruct on CTIBench-RCM and CTIBench-MCQA while remaining competitive on general instruction-following.
-
Vulnerability Mitigation System (VMS): LLM Agent and Evaluation Framework for Autonomous Penetration Testing
An LLM agent with planner and summarizer modules solved roughly a third of PicoCTF and OverTheWire CTF challenges, and the authors release the agent and benchmarks.
-
APT-LLM: Embedding-Based Anomaly Detection of Cyber Advanced Persistent Threats Using Large Language Models
APT-LLM turns process-event traces into sentences, embeds them with BERT-family models, and uses autoencoder reconstruction error to detect APTs, reporting AUC gains over OC-SVM, DBSCAN, and Isolation Forest.
-
On the Surprising Efficacy of LLMs for Penetration-Testing
A critical review arguing that LLMs are surprisingly effective for penetration testing because the task is largely pattern-matching, while noting serious reliability, safety, and cost barriers to autonomous use.
Discussion (0). Sign in to comment.