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24/07/2026 4:10 pm
TL;DR: Anthropic’s August 2025 Threat Intelligence Report describes AI-driven extortion across 17 organisations, AI-assisted fraud and employment schemes, and criminal misuse of Claude Code, showing how LLMs are being folded into real attack workflows, according to Noma Security. The key shift is that LLM security now requires inventory, red-teaming, and runtime controls, not just model safety reviews.
NHIMG editorial — based on content published by Noma Security: Anthropic’s August 2025 Threat Intelligence Report and its LLM security implications
Questions worth separating out
Q: How should security teams govern AI models that can call tools and access data?
A: Security teams should govern AI models as non-human identities with named owners, limited scope, short-lived credentials, and continuous authorization.
Q: Why do conversational AI systems create new identity and access risks?
A: Because they can combine data retrieval, decision-making, and execution in a single interaction.
Q: What do enterprises get wrong about AI red teaming maturity?
A: Many teams stop at attack simulation and assume the test itself is the control.
Practitioner guidance
- Inventory AI systems as governed assets Create a complete register of production and shadow AI use cases, including data sources, connectors, secrets, and tool permissions.
- Build an AIBOM for every production use case Document the model, training and retrieval dependencies, prompt templates, API connections, and external services behind each AI deployment.
- Enforce runtime policy on tool use and data access Set explicit allow and deny rules for prompt handling, tool invocation, output filtering, and data retrieval.
What's in the full article
Noma Security's full article covers the operational detail this post intentionally leaves for the source:
- Case-by-case examples of how Claude was abused across extortion, fraud, and malware workflows.
- The specific detection and classification approaches the vendor used to identify suspicious usage patterns.
- The incident response actions the vendor took, including account bans and intelligence sharing.
- The article’s full discussion of AI governance and runtime security controls in practice.
👉 Read Noma Security's analysis of Anthropic's August 2025 threat intelligence report →
LLM security and AI-driven crime: what security teams must change?
Explore further
24/07/2026 4:15 pm
LLM security is now an access-governance problem, not just a content-safety problem. Once models can retrieve data, call tools, or support decisions, they become systems with effective privileges. That means the governing question is what an AI system can reach, change, or trigger, not just whether its outputs are safe. Practitioners should treat model access as a governed entitlement boundary.
A question worth separating out:
Q: Who is accountable when an AI system makes a harmful decision?
A: Accountability should follow the identity chain that authorized, configured, or triggered the action, including the human owner, the platform team, and any delegated agent or tool account. If the organisation cannot name that chain, the governance model is too weak for regulated AI use.
👉 Read our full editorial: LLM security is now a governance problem, not just a model problem
