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An Evidence-First Multi-LLM Framework for Auditable Critical-Infrastructure Dependency Modeling
Evidence-first multi-LLM framework builds auditable critical-infrastructure dependency graphs while preserving provenance and unresolved cases.
The framework constructs Infrastructure Knowledge Bases and Infrastructure Dependency Graphs from heterogeneous infrastructure documentation using multiple open-weight LLMs that independently extract candidate entities and dependencies from normalized evidence. It separates evidence verification, ontology grounding, entity resolution, dependency alignment, validation, fusion, and human review, projecting the validated IKB deterministically into the IDG without new LLM-generated knowledge. Evaluation across nine infrastructure projects shows entity recovery achieves substantially higher recall than full dependency recovery, and cross-model overlap is much lower for dependencies than entities, indicating models often produce non-overlapping candidate assertions rather than stable consensus.
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Turn it off and on again, but for critical infrastructure
KTH researchers trained a reinforcement-learning intrusion response agent on an emulated segmented OT network that autonomously resets hosts and processes to disrupt intruders.
Researchers at KTH Royal Institute of Technology built a containerized replica of a segmented industrial network, attacked it across 14 days, and captured 40,000 30-second traffic intervals to train a defense agent under partial observability. The agent observes six packet-count numbers per interval, maintains 500 running state hypotheses, and can reset supervisory hosts, water tank processes, or entire subnets, with resets rebooting the target, renewing credentials, and changing its IP. The best agent approached a full-visibility baseline but depends on an assumed attacker behavior model; the testbed comprised three supervisory hosts, two PLCs, two tanks, weak credentials, and CVE-2017-7494 exposure. The team released its implementation and plans validation on a real industrial testbed with a partner.
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Risky Bulletin: Academics find source code overlaps between Geedge and China's Great Firewall
Academics linked Chinese vendor Geedge Networks' Tiangou Secure Gateway source code to one of the Great Firewall's three traffic filtering capabilities.
US researchers presenting at USENIX Security reconstructed Geedge Networks' Tiangou Secure Gateway firmware from over 100,000 leaked files, including Git repositories with commit history, and matched its filtering behavior to sections of China's Great Firewall. They found only 1 of 3 characterized DNS injectors matched Geedge code, noted the system relies on memory-unsafe C components and copied third-party code, and said its bugs could aid future circumvention tools. Geedge also exports censorship tools to Kazakhstan, Ethiopia, Pakistan, and Myanmar. The newsletter additionally rounds up multiple breaches.
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Mapping out your unknown: A threat hunter’s guide to GitHub
Datadog Security Labs publishes a threat-hunting guide with audit-log queries to detect GitHub token theft, device code phishing, and source code exfiltration.
Datadog's threat-hunting guide covers GitHub audit log queries for detecting compromised accounts, stolen personal access tokens, and malicious OAuth app authorizations. Attackers typically obtain credentials through phishing, credential stuffing, leaked secrets, or device code phishing, then map private repositories, exfiltrate source code, and pivot into connected cloud and CI/CD environments. The guide maps detections to MITRE techniques like T1078 and T1528 and documents GitHub logging quirks affecting attribution, token metadata, and visibility fields.
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Most Firms Unable to Recover Quickly from Ransomware
Fenix24's first State of Recoverability report finds only 0.5% of 800+ ransomware clients neared 24-48 hour recovery targets, with identity failures central.
Drawing on 500+ ransomware recoveries, Fenix24 found only four of 800+ clients (0.5%) came close to their own 24-48 hour recovery targets, and none reached full operations for weeks. 99.2% lacked a documented identity recovery plan, Active Directory typically fell first, and 94% tied backup systems to the compromised directory. In 38% of engagements backups survived but could not carry recovery; storage ran short in 82% of cases and 95% lacked meaningful MFA on critical infrastructure consoles.
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$536 and 8 Hours: AI Learns to Attack a Different PLC
Forescout used Claude to port a WAGO PLC exploit for $536 in 8 hours, and an AI-generated payload later permanently bricked the test device.
Forescout researchers used Claude Code with Ghidra, terminal access, and physical hardware to port CVE-2021-31886, a pre-authentication buffer overflow in the Nucleus FTP server, from a WAGO 750-852 PLC to the WAGO 750-831 without source code or debugger access. The final RCE development stage cost $535.74 in API fees over an 8-hour, 32-minute session (2.6k input and 1.3M output tokens) and required heavy researcher guidance. Claude produced working ICMP and UDP beacon exploits in 12 minutes after identifying that normal FTP command completion was overwriting its shellcode, fixed by omitting the CRLF terminator. A follow-up session extending the payloads toward a C2 implant wrote to flash-mapped memory and permanently bricked the PLC, underscoring the risks of autonomous agents operating against physical infrastructure.
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First Galileo SAS Authenticated Time Solution
Researchers demonstrate the first timing solution computed from Galileo's new Signal Authentication Service, protecting receivers against GNSS spoofing.
Galileo's new Signal Authentication Service (SAS) on the E6-C signal directly authenticates ranging measurements, closing a gap left by OSNMA, which only protects navigation data. Researchers built a snapshot software receiver implementing a simplified SAS protocol and computed an authenticated receiver clock bias from recordings of both SAS-capable satellites, collected with a Septentrio SDR prototype. The results demonstrate feasible authenticated timing ahead of full SAS operational deployment.
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Superusers Need Super Protection: How to Bridge Privileged Access Management and Identity Management
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Risks in IoT Supply Chain
Unit 42 analyzes multilayer IoT supply chain risks across hardware, firmware, and software, citing counterfeit Cisco switches and OpenWrt attacks.
Unit 42 examines weaknesses in the IoT supply chain ecosystem across hardware, firmware, operation, and vulnerability layers, noting that 89% of IT decision-makers reported IoT device growth and IDC forecast 41.6 billion connected IoT devices by 2025. Examples include counterfeit Cisco Catalyst 2960-X switches with possible backdoor access (F-Secure, July 2020), a March 2020 OpenWrt flaw enabling malicious update impersonation, and threat actor interest in TeamViewer remote support software. The report stresses that untracked third-party components and missing device inventories make it hard to assess vulnerability impact across vendors.
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Bridging the First-Hour Gap: Evaluating AI Reliability and Benchmarking Deficiencies in Cyber Incident Response for Law Enforcement
Survey of playbooks, LLMs, RAG, and agentic AI for law-enforcement cyber first responders finds RAG most viable but benchmarks inadequate for legal requirements.
The paper surveys decision-support architectures (playbooks, LLMs, RAG frameworks, agentic AI) for frontline law enforcement during the first hour of a cyber incident, where volatile digital artifacts risk procedural errors and evidence attrition. RAG-based systems are identified as a relatively viable intermediate solution, though prompt sensitivity and confident hallucinations in legal contexts pose major risks. The authors find current cybersecurity benchmarks insufficient for law enforcement safety and legal demands, and argue for a new benchmark focused on naive query robustness and evidence preservation.
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