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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesAI-assisted cyber espionage is intelligence-gathering activity in which attackers use generative AI to help with some part of an operation. Public threat-intelligence reports describe uses such as researching targets, writing or troubleshooting code, translating text, and creating phishing content. Those reports mostly show AI helping people do familiar tasks—not autonomous attacks or proof that AI makes intrusions more effective.
What makes cyber activity espionage?
The defining feature is the objective: collecting information for intelligence purposes. An operation may involve researching a target, gaining access to systems, moving through a network, or taking data. Using AI does not change that objective, and the presence of AI alone does not make an incident espionage. The same kinds of tools could be used in financially motivated crime or influence operations.
Generative AI is one possible aid within a broader operation. It may help an operator understand information, draft material, or work through a technical problem; the operator, infrastructure, access methods, and other tools still matter. A report that an actor used an AI service for one task does not establish that the entire operation was AI-driven.
How do attackers use generative AI?
Public reporting most often describes assistance with tasks that already exist in cyber operations. The table summarizes examples reported by Microsoft and Google; it does not imply that every actor used every technique or that the techniques succeeded.
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| Use | Where it can fit | What public reporting says |
|---|---|---|
| Target and infrastructure research | Preparation and reconnaissance | Microsoft’s February 2024 report described reconnaissance support. Google Threat Intelligence Group (GTIG) reported in January 2025 that actors used Gemini for research into infrastructure and hosting, target reconnaissance, and vulnerability research. |
| Code assistance and scripting | Preparation or technical work during an operation | Microsoft reported coding support. GTIG’s January 2025 analysis described code troubleshooting, payload development, malicious scripting, and requests for evasion assistance. |
| Language and content work | Research, communication, or phishing preparation | Microsoft described help with human and machine languages. GTIG’s 2025 tracker reported phishing-lure creation; OpenAI’s June 2025 report described investigated cases involving social engineering. |
| Support for later operation stages | Post-compromise activity and data movement | GTIG’s 2025 tracker described observed uses involving lateral movement, command-and-control (C2) support, and exfiltration research. These are reports of particular activity, not a complete account of all actors. |
| LLM use by malware while it runs | Execution, rather than only an operator’s preparation | GTIG’s 2025 tracker named PROMPTFLUX and PROMPTSTEAL as early examples of malware using LLMs during execution. Google Cloud and Mandiant also described runtime LLM use as a later 2025 development. |
Sources: Microsoft Security, February 14, 2024; Google GTIG, January 2025; Google GTIG, 2025; OpenAI, June 2025; Google Cloud/Mandiant, 2025.
Does AI make cyber espionage more capable or autonomous?
The reporting supports a measured answer: AI can provide productivity or technical assistance, but task assistance is not evidence of a new level of capability. In its January 2025 analysis of attempts to misuse Gemini, GTIG said it had not seen novel capabilities in the activity it analyzed. It concluded: “While AI can be a useful tool for threat actors, it is not yet the game-changer it is sometimes portrayed to be.” That assessment is specific to GTIG’s observations at that time, not a guarantee about every actor or later development.
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Microsoft said its February 2024 research with OpenAI had not identified significant attacks employing the LLMs it monitored closely. That statement, too, describes the providers’ visibility and period of observation; it does not establish that no other AI-assisted activity occurred.
The runtime malware examples reported by GTIG in 2025 are a distinct, developing technique. They should not be conflated with an attacker asking a model for research or code help. Nor does the existence of experimental malware demonstrate that an operation can plan and execute itself, or that the technique is widespread or effective.
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What do reports establish about how common this is?
They establish that specific providers observed or investigated some misuse—not a global rate for AI use in cyber espionage. Microsoft’s 2024 Cyber Signals report named state-affiliated groups that sought to use LLMs to augment ongoing cyber operations. Google’s 2025 tracker described continued misuse by state-sponsored actors, including activity attributed to North Korea, Iran, and the People’s Republic of China. OpenAI’s June 2025 report covers cases it investigated and disrupted involving misuse of its services; those cases are not prevalence estimates for the wider threat landscape.
Numbers about targeted sectors should not be mistaken for AI-espionage statistics. Microsoft’s 2024 Digital Defense Report showed 24% of reported attacks targeting IT, 21% targeting education and research, and 12% targeting government. Those are sector proportions in that report, not estimates of the share of attacks using AI or of cyber-espionage activity specifically.
Reports also differ in which services, investigations, actors, and time periods they cover. A generated message or code sample by itself cannot prove that an attacker used AI. A careful account attributes an observation to the organization that reported it and avoids treating separate vendors’ examples as one consistent census.
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What should defenders do?
AI use does not remove the value of controls that address phishing, stolen credentials, compromised devices, and suspicious movement of data. Microsoft’s February 2024 report describes using AI-enabled detection for changes in resource or network traffic, behavioral analytics for risky sign-ins and anomalous behavior, machine-learning malware detection, Zero Trust controls, and device-health verification. These are methods Microsoft describes, not a guarantee that any product or checklist will prevent an intrusion.
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- Reduce the chance of account compromise: use phishing-resistant authentication where available, apply least privilege, and review access to sensitive systems and data.
- Check identity and device context: investigate unusual sign-ins, unexpected privilege changes, and devices that fail established health requirements.
- Look for behavior across the operation: correlate network or resource changes, suspicious scripts or processes, lateral movement, and unusual data access rather than relying on whether content appears AI-generated.
- Make reporting and escalation clear: give staff a straightforward way to report suspected phishing or unusual access, and ensure security responders know how to investigate and contain it.
These practices address familiar intrusion behaviors whether or not an attacker used a generative model. The cited vendor reporting does not establish a separate defensive control that reliably identifies AI-assisted espionage on its own.
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