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The Dutch Institute for Vulnerability Disclosure (DIVD) disclosed that it detected and confirmed an intrusion into its infrastructure on 25 September 2026. DIVD isolated affected infrastructure, engaged an external incident-response team for forensic investigation, and directly notified parties it identified as affected.
DIVD reported the incident to the Dutch Data Protection Authority and the National Cyber Security Centre and consulted police. The organization said the intrusion may have involved an agentic-AI-driven attack, but stressed that the investigation remains ongoing, no threat actor has been identified, and it is operating under an assume-breach, worst-case posture.
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On 25 September 2026, the Dutch Institute for Vulnerability Disclosure (DIVD) disclosed that it had detected and confirmed an intrusion into its infrastructure. DIVD isolated its infrastructure, engaged a third-party incident-response team for forensics, notified affected parties, reported the matter to Dutch authorities, and consulted police.
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A financially motivated lone attacker used AI-assisted workflows to breach a large **AWS** environment in roughly 72 hours, according to a Sygnia incident-response report, chaining weaknesses across Internet-facing applications, AWS resources, source code repositories, **CI/CD** pipelines, runtime components, and data stores. The intrusion reportedly began with the theft of an AWS access key and expanded through secrets harvesting, cloud enumeration, backdoor creation, data exfiltration, deployment pipeline abuse, runtime modification, database access, and operational disruption. To extort the unnamed global enterprise, the actor carried out mostly reversible actions including denying access to **S3** buckets, scaling **ECS** services to zero, blocking network access with ACL rules, and purging **SQS** queues. The incident reflects a broader shift from isolated malicious use of generative AI to **agentic AI** supporting larger portions of the attack chain. Research cited by OALABS and observations from Anthropic indicate attackers are using tools such as **Claude** and **OpenAI Codex** to automate reconnaissance, analyze stolen data, generate code, and adapt operations during live intrusions, lowering the barrier for less-skilled actors while helping experienced operators scale activity. Security researchers said the trend now spans multiple **MITRE ATT&CK** tactics, including reconnaissance, credential theft, malware development, and social engineering, pushing defenders to prioritize detection of AI-enabled workflows, stronger identity controls, broader visibility, and faster automated containment.
Jul 9, 2026
Hugging Face disclosed that an **autonomous AI agent system** breached part of its production infrastructure after a malicious dataset exploited two code-execution paths in the company’s dataset-processing pipeline. The intrusion reportedly achieved code execution on a processing worker, escalated to node-level access, harvested cloud and cluster credentials, and moved laterally across internal clusters at machine speed. Hugging Face said the attacker accessed a limited set of internal datasets and several service credentials, while reporting **no evidence** of tampering with public models, datasets, Spaces, or its software supply chain. The company said it closed the exploited execution paths, rebuilt compromised nodes, rotated secrets, tightened cluster controls, and notified law enforcement while continuing to review possible partner and customer data exposure. In its incident write-up, Hugging Face said AI-assisted detection and LLM-driven forensics helped reconstruct more than **17,000 attacker events**, and outside reporting linked the breach to a broader pattern of AI-driven intrusions in which autonomous agents can rapidly adapt, recover from errors, escalate privileges, and compress defender response times, raising concern over hybrid human-plus-AI attack operations.
Sep 6, 2026
Google-owned Mandiant disclosed an internal AI-assisted vulnerability discovery platform, the **Agentic Vulnerability Discovery Harness (AVDH)**, saying it identified more than 100 verified high-severity software flaws in two days during a live investigation tied to stolen corporate repositories. Mandiant said the system has been used internally for about ten months, scanned tens of millions of lines of code, and produced tens of thousands of findings, including dozens of assignable vulnerabilities affecting widely used web extensions and open-source projects. Mandiant said the work has already led to 12 assigned CVEs, including `CVE-2026-13242` and `CVE-2026-55803`, with roughly another dozen cases still in coordinated disclosure. The company described AVDH as a pipeline of specialized AI agents handling threat modeling, entry-point discovery, context enrichment, hypothesis generation, and validation, while stressing that human analysts still review threat models and manually reproduce exploits before findings are accepted. To reduce false positives, agents are designed to challenge one another’s conclusions and apply consultant-authored rules by language, framework, and vulnerability type; Mandiant also said it evaluated the system on synthetic intentionally vulnerable codebases rather than public benchmarks to avoid model-training contamination.
Aug 19, 2026