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What is the difference between AI-assisted and traditional cyberattacks?
A traditional cyberattack is usually described by what the attacker is trying to do or the method used: steal credentials, exploit a vulnerability, deceive someone into sending money, or deploy ransomware. An attack does not stop being phishing because an attacker used an AI model to draft the message.
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An AI-assisted attack uses AI at one or more points in that familiar workflow—for example, to generate content, analyze data, personalize a lure, or automate a task. An attack on an AI system instead tries to manipulate, compromise, or extract information from the AI system. Keeping these categories separate helps avoid treating every use of AI in an attack as an autonomous AI cyberattack.
| Question | Traditional attack without AI assistance | AI-assisted attack |
|---|---|---|
| What is the attacker’s objective? | May be credential theft, fraud, intrusion, or another familiar goal. | Can be the same objective; using AI does not define the goal. |
| How is a tactic carried out? | People and conventional tools create and deliver the attack. | AI may help with content, analysis, personalization, or automation alongside conventional tools. |
| What happens to human involvement? | People may make decisions or perform steps throughout the workflow. | AI can assist with some work, but current evidence does not establish universal end-to-end autonomy. |
| What is exposed? | People, accounts, devices, networks, and services may be targets. | The same targets remain relevant; deployed AI systems and their dependencies can also add exposure. |
This is a practical way to compare workflows, not a claim that every attack fits neatly into one column. Attackers can combine AI models, websites, social media accounts, and other tools. OpenAI’s 2026 account of malicious-use cases describes AI being used alongside such conventional infrastructure rather than replacing it.
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How can AI change phishing and social engineering?
AI can help produce persuasive messages and tailor them to a target, which can support phishing and other social-engineering efforts. Canada’s National Cyber Threat Assessment 2025–2026 says threat actors use generative and predictive AI, including large language models, for tasks such as content generation and big-data analysis. It also says AI can help make social engineering more personalized and convincing, including through audio or visual content that impersonates a trusted person.
The Canadian assessment quotes its cited source as saying: “Throughout 2024, adversaries increasingly adopted [generative AI], especially as a part of social engineering efforts”. That is evidence of increasing adoption in this area, not proof that every phishing campaign uses AI or that AI-generated messages are inherently successful.
AI can also be used to probe safeguards in generative AI systems. The U.S. Government Accountability Office describes roleplaying prompts, gradual steering through apparently benign steps, and automated prompt refinement using multiple generative AI systems. These techniques can help misuse or bypass safeguards; by themselves, they do not show that a model then executes every step of a cyberattack autonomously.
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Can AI conduct a cyberattack on its own?
The evidence calls for a careful answer. The International AI Safety Report 2026 says one AI developer reported a threat actor using its models to automate 80–90% of the effort in an intrusion. The report says people remained involved at critical decision points; it does not describe a fully autonomous, end-to-end real-world attack.
The report also discusses researchers demonstrating network probing in laboratory settings. It says general-purpose AI systems had not been reported to conduct end-to-end cyberattacks in the real world. A reported case and a laboratory demonstration show meaningful capability, but they should not be generalized into a claim that all AI systems can independently plan and complete intrusions.
What does it mean to attack an AI system?
When AI itself is the target, the attacker is trying to affect the system’s behavior, compromise it, or obtain information from it. NIST’s 2025 Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations distinguishes categories including evasion, poisoning, privacy, and misuse attacks against generative AI systems. Its taxonomy also covers evasion, poisoning, and privacy attacks against predictive AI systems.
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These categories are distinct from using an AI model to write a phishing message. They matter to the organizations that design, deploy, evaluate, or govern AI systems because an AI service can become part of the organization’s attack surface. ENISA’s 2026 threat-landscape overview describes this dual role: malicious groups use AI to facilitate or enhance activity, while AI systems integrated into businesses can themselves attract exploitation.
Do the available numbers show that AI attacks are worse?
No like-for-like rate or loss comparison in the available evidence establishes that AI-assisted attacks are more common or more damaging than traditional attacks. The figures below illuminate different issues, but they measure different things and should not be added together or treated as a direct comparison.
| Figure | What it measures—and what it does not |
|---|---|
| 80–90% of intrusion effort | A developer-reported case described in the International AI Safety Report 2026: the share of effort automated in one intrusion. Human involvement remained at critical decision points; this is not a general measure of autonomous attacks. |
| More than 95% reduction in phishing costs | An academic study’s estimate, as summarized by the U.S. Government Accountability Office in Science & Tech Spotlight: Malicious Use Of Generative AI. It concerns estimated costs for malicious users, not phishing costs for all attackers, attack frequency, or success rates. |
| More than 48,000 new CVE identifiers in 2025, a 22% increase from 2024 | ENISA’s 2026 threat-landscape announcement reports newly assigned vulnerability identifiers. CVEs count disclosed vulnerabilities, not successful cyberattacks. |
| 138 publicly reported generative AI incidents resulting in harm or near harm worldwide for 2024 | Canada’s National Cyber Threat Assessment 2025–2026 reports this total as predicted from the first six months of 2024. It includes incidents across categories and is not a count of cyberattacks alone. |
Read each figure according to its definition and scope. A vulnerability count, an estimate of phishing costs, a reported intrusion case, and a total of AI-related incidents cannot answer the same question.
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How should organizations defend against both kinds of risk?
Keep conventional security controls in place
Since AI can support familiar attack workflows rather than replace them, organizations still need to protect accounts, devices, networks, and services against established threats. Security planning should also include an inventory of deployed AI systems and their dependencies, so those systems are not overlooked as new parts of the attack surface.
Test AI applications and layer safeguards
NIST discusses both mitigations and their limitations. The U.S. Government Accountability Office describes approaches including filtering user instructions, reinforcing safeguards through human feedback, and using a separate generative AI system to detect malicious inputs. These are controls to test and combine, not guarantees that a system cannot be manipulated.
Do not rely on a single AI detector
A detector aimed at AI-generated content cannot identify every AI-assisted attack: AI may have helped with analysis or another task without generating the material a detector examines. Treat detection as one possible layer, not a substitute for broader security controls and testing.
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