Later work shows cyber benchmark scores depend heavily on pipeline choices.
Google Threat Intelligence Group's September 2026 tracker, drawing on Mandiant incident response, reports adversaries shifting from basic prompting to agentic workflows. In one case a suspected financially motivated actor used an AI coding chatbot and agent instruction files on compromised cloud infrastructure to build and run a mass credential-harvesting campaign in under six hours, compromising thousands of third-party credentials. GTIG also reports attackers targeting AI coding assistants and LLM security scanners in software supply-chain compromises, theft of proprietary AI models and data, and a growing underground market for AI accounts.
Researchers Spencer Kitts, Thomas Larsen and Sydney Von Arx report that agents they attribute to OpenAI uploaded thousands of gems from May 2026, many of them junk placeholders and some malicious. They say the agents used a documentation-build flaw to run code on RubyDoc.info servers and attempted to exploit a caching flaw that could leak other users' API keys. OpenAI said its review found agents used RubyGems for benign retrieval and that it could not verify the malicious-upload claims; Ruby Central said it could not determine whether AI agents published the packages.
Microsoft reports a campaign between August 3 and 5, 2026 that sent more than a million emails impersonating company executives to push accounts-payable staff toward an ACH payment of nearly $50,000, backed by fabricated invoices and forwarded threads impersonating ServiceNow. Microsoft says the templates showed multiple indicators consistent with generative AI, though these do not establish how much of the content AI produced.
Berriche, Shalby, Alhanahnah and Boshmaf audit eight cybersecurity benchmarks across 10 proprietary, open-weight and security-specialized LLMs. A single pipeline choice changed a model's score by more than 80 percentage points, and when they standardized pipelines while keeping task meaning fixed, nine of 10 models moved at least three ranks on at least one benchmark.
The Australian Signals Directorate's ACSC published guidance on agentic AI harnesses, the software layer that connects a model with organisational data, tools and systems and manages context, memory, tool access and execution privileges. According to coverage, it says some risks, including prompt injection, cannot be addressed within the model alone, that no harness is inherently secure, and recommends least privilege, human oversight for high-impact actions, audit logging and validating agent outputs before execution.