REVIEW 8 cited by
Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report
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
As transformer-based large language models (LLMs) increasingly permeate society, they have revolutionized domains such as software engineering, creative writing, and digital arts. However, their adoption in cybersecurity remains limited due to challenges like scarcity of specialized training data and complexity of representing cybersecurity-specific knowledge. To address these gaps, we present Foundation-Sec-8B, a cybersecurity-focused LLM built on the Llama 3.1 architecture and enhanced through continued pretraining on a carefully curated cybersecurity corpus. We evaluate Foundation-Sec-8B across both established and new cybersecurity benchmarks, showing that it matches Llama 3.1-70B and GPT-4o-mini in certain cybersecurity-specific tasks. By releasing our model to the public, we aim to accelerate progress and adoption of AI-driven tools in both public and private cybersecurity contexts.
Forward citations
Cited by 8 Pith papers
-
LLM-Based Scientific Equation Discovery via Physics-Informed Token-Regularized Policy Optimization
PiT-PO adaptively fine-tunes an LLM during symbolic regression search using physics-validity and token-level redundancy constraints, reporting state-of-the-art benchmark results and a periodic-hill turbulence closure.
-
Antares: Foundation Models for Agentic Vulnerability Localization
Antares-3B, a 3B model trained with SFT plus GRPO, matches GPT-5.5 on repository-scale vulnerability localization at roughly 1/100th the inference cost.
-
MOT-SR: Multi-Objective Tool-Augmented Scientific Equation Discovery with Large Language Models
MOT-SR combines tool-augmented data analysis with multi-objective Pareto selection to discover symbolic equations, outperforming LLM-based and classical SR baselines on benchmarks and an EMRI orbital-correction task.
-
DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving
DriveCode's continuous number projector and regression number head reduce control-signal errors in LLM autonomous driving compared with text-token and xVal baselines on the tested datasets.
-
EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora
EraRAG uses hyperplane-based locality-sensitive hashing to build a hierarchical retrieval graph whose affected regions only are re-summarized when new documents arrive, cutting update cost by up to an order of magnitude.
-
DrSR: LLM based Scientific Equation Discovery with Dual Reasoning from Data and Experience
DrSR improves LLM-based symbolic regression by adding data-aware structural insights and a reflective idea library, beating prior methods on six benchmark tasks.
-
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.
-
Less Data, More Security: Advancing Cybersecurity LLMs Specialization via Resource-Efficient Domain-Adaptive Continuous Pre-training with Minimal Tokens
DAP with 118.8M tokens improves a 70B LLM on cybersecurity benchmarks, but the claimed state-of-the-art data efficiency is not supported by the controlled comparisons.
Discussion (0). Continue with ORCID to comment.