GenTI: Benchmarking LLMs for Autonomous IDPS Rule Generation for Unseen Attacks

Rule-based Intrusion Detection and Prevention Systems IDPS offer precise attack detection as well as mitigation, however their manually crafted, signature-driven rules limit adaptability to emerging and zero-day threats. Additionally, existing public datasets e.g., CICIDS2017, UNSW-NB15 focus on...

CTI-REALM: A new benchmark for end-to-end detection rule generation with AI agents

Excerpt: CTI-REALM is Microsoft’s open-source benchmark for evaluating AI agents on real-world detection engineering—turning cyber threat intelligence CTI into validated detections. Instead of measuring “CTI trivia,” CTI-REALM tests end-to-end workflows: reading threat reports, exploring telemetr...

RulePilot: An LLM-Powered Agent for Security Rule Generation

The real-time demand for system security leads to the detection rules becoming an integral part of the intrusion detection life-cycle. Rule-based detection often identifies malicious logs based on the predefined grammar logic, requiring experts with deep domain knowledge for rule generation...

GRIDAI: Generating and Repairing Intrusion Detection Rules Via Collaboration among Multiple LLM-Based Agents

Rule-based network intrusion detection systems play a crucial role in the real-time detection of Web attacks. However, most existing works primarily focus on automatically generating detection rules for new attacks, often overlooking the relationships between new attacks and existing rules, which...

FALCON: Autonomous Cyber Threat Intelligence Mining with LLMs for IDS Rule Generation

Signature-based Intrusion Detection Systems IDS detect malicious activities by matching network or host activity against predefined rules. These rules are derived from extensive Cyber Threat Intelligence CTI, which includes attack signatures and behavioral patterns obtained through automated tool...

VerilogLAVD: LLM-Aided Rule Generation for Vulnerability Detection in Verilog

Timely detection of hardware vulnerabilities during the early design stage is critical for reducing remediation costs. Existing early detection techniques often require specialized security expertise, limiting their usability. Recent efforts have explored the use of large language models LLMs for...

DHCP Pool Exhaustion

github.com/lxc/incus is vulnerable to DHCP Pool Exhaustion. The vulnerability is due to improper generation of nftables rules for local services when ACLs are used on devices connected to a bridge, which allows bypassing security.macfiltering, security.ipv4filtering, and security.ipv6filtering...

Incorrect Authorization

Overview Affected versions of this package are vulnerable to Incorrect Authorization via the nftables rule generation process. An attacker can gain unauthorized access to network traffic and impersonate other virtual machines or containers by exploiting the partial bypass of security filtering...

Automatically Generating Rules of Malicious Software Packages Via Large Language Model

Today's security tools predominantly rely on predefined rules crafted by experts, making them poorly adapted to the emergence of software supply chain attacks. To tackle this limitation, we propose a novel tool, RuleLLM, which leverages large language models LLMs to automate rule generation for O...

Protecting Your Web Apps with AppSpider Defend Until They Can Be Patched

AppSpider scans can detect exploitable vulnerabilities in your applications, but once these vulnerabilities are detected how long does it take your development teams to create code fixes for them? In some cases it could take several days to weeks before a fix/patch to resolve the vulnerability ca...