Detecting and countering misuse of AI: September 2026
Over the past eight months, our Threat Intelligence team identified and disrupted operations in which threat actors tried to use Claude for malicious activity. In this report, we share case studies from those operations and describe how malicious use of Claude has evolved since our previous threat reports in
March,
August, and
November 2025. In each case, we disrupted the activity, used what we learned to strengthen our safeguards, and shared intelligence with authorities and industry partners, where appropriate.
This report covers activity we disrupted between December 2025 and August 2026 across seven harm areas: cyber operations, influence operations, surveillance, scams and fraud, biological misuse, conventional weapons development, and distillation. Claude Haiku, Sonnet, and Opus models were used. None of the misuse cases involved the use of Claude Fable or Mythos-class models, with the exception of one illicit distillation case.
The cases we share here aren’t typical misuse, but rather examples of the most notable and novel threat activity we’ve identified to date. We’re publishing this work because we believe we have a responsibility to disclose malicious misuse of our services. As models become increasingly capable, their risks will increase, unless AI developers and society’s defenders act to make them safer.
The threat actors covered in this report include suspected state-sponsored groups, financially motivated criminals, commercial spyware vendors, state propaganda institutions, and politically motivated individuals. The cases range from a network of fake dating apps designed to defraud users to surveillance systems built to identify and monitor dissidents.
Sophisticated and persistent threat actors continuously test our safeguards and try to circumvent the technical measures we use to detect and prevent misuse. We’ll continue to evolve our safeguards and coordinate with our partners to improve our ability to detect, disrupt, and
prevent future misuse.
We hope that the findings in this report will help other developers recognize similar patterns on their own platforms, give governments and civil society a clearer view of how emerging threats take shape, and strengthen collective defenses.
- GTG-20006: Russian espionage
- GTG-50014: ShinyHunters smash-and-grab opportunists
- GTG-10007: Exploit foundries and autonomous attack frameworks
- AI supply chain as target, loot, and attack compute
- GTG-50020: From hotel bookings to the AI supply chain
- GTG-50029: Hacktivists targeted European political and affiliated entities
- Prevailing trends
AI-augmented cyber operations
Cyber operations: From assistant to orchestrator
Over the past six months, our Threat Intelligence team identified and disrupted a series of cyber operations in which threat actors used Claude. The actors included suspected state-sponsored groups, financially motivated criminals, and politically motivated individuals. This section presents some of those cases.
Throughout these case studies, the report will reference Generative Threat Groups (GTGs). These are Anthropic’s internal designators for actors observed to be abusing AI. The report also attempts to measure uplift, a term we use to describe the AI capability boost, or how much more harm was caused with AI versus without AI. We view uplift through the lens of speed, scale, and depth, and attempt to determine how an actor’s adoption of AI meaningfully impacts each of these traits.
Many commentators focus on the risk of AI developing exploits at scale. While this is a danger, the risk from AI adoption is more pronounced across the cyber kill chain, where adversaries can operate faster, across a broader and deeper surface area, with fewer resources.
The cases span the period from December 2025 through August 2026. In all cases, Claude Haiku, Sonnet, and Opus models were used; no malicious activity was found on Claude Fable or Mythos (which has a
series of safeguards in place that greatly reduce its ability to perform harmful cyber tasks). In each case we disrupted the activity involved, strengthened our AI safeguards based on what we learned, and shared intelligence with authorities and industry partners where appropriate.
In the following report, we begin by discussing the key trends that we’ve observed in these cyber operations, then move to reporting the case studies and how they highlight those trends.
Trends
Sophisticated attacks no longer require sophisticated attackers
The cybersecurity skills of AI models means that AI has collapsed the labor and tooling gap that used to separate well-resourced, state-sponsored operations from individual operators. In the case studies we report below, a hacktivist using stolen API keys, disparate financially motivated individuals, and a state espionage operator each sustained multi-victim campaigns that, even just a year ago, would have required many skilled operators and specialist knowledge.
For threat intelligence investigators, sophistication has stopped being a reliable signal of who is behind an operation. Every layer of offensive operations has been uplifted by AI, from reconnaissance and tool development to data processing and exploitation. An example of this uplift in capabilities is documented in case study GTG-50014 (described below). The net effect of this uplift in capabilities is access to an increased
breadth and
depth of knowledge, which in turn drives
increased speed of capability development and implementation.
more....
https://www.anthropic.com/threat-intelligence-report-september-2026
www.foxnews.com
