Understanding malicious AI

Artificial intelligence (AI) is transforming industries, from healthcare and finance to education and cybersecurity. But as AI becomes more powerful and accessible, it is also being weaponized. What was once a tool for productivity and innovation is increasingly being used for deception, manipulation, and cybercrime.

An article by SQ Magazine found that “the number of reported AI-enabled cyber attacks rose by 47% globally in 2025.” The article also notes that “healthcare, a critical sector, saw a 76% rise in targeted AI attacks in 2025, largely attributed to the automation of ransomware deployment.”

While AI was not intended for ill use, it allows attackers to operate faster, target more precisely, and scale their efforts in ways that were previously impossible. As a result, traditional warning signs of cyber threats are becoming less obvious, and distinguishing between legitimate and malicious AI is increasingly challenging.

So how do you tell the difference between helpful AI and harmful AI?

What is malicious AI?

Malicious AI refers to artificial intelligence systems that are intentionally designed, manipulated, or exploited to cause harm. This harm can range from financial fraud and data theft to misinformation and privacy violations.

Unlike traditional cyberattacks, malicious AI is:

• Scalable: It can target thousands of victims simultaneously
• Adaptive: It learns and improves over time
• Deceptive: It mimics human behavior convincingly

Cybercriminals are now using AI to generate highly personalized phishing messages, deepfake videos, and even malware code. In some cases, AI systems themselves are attacked or manipulated through techniques like prompt injection or data poisoning.

Why malicious AI is harder to detect

Traditional cyber threats often leave obvious clues, poor grammar, suspicious links, or unusual system behavior. Malicious AI, however, is designed to blend in.

Here’s why detection is becoming more difficult:

Human-like communication

AI can produce natural, fluent language that closely mimics human communication, making it increasingly difficult to distinguish between genuine and malicious interactions. As noted in the study ‘Comparing Large Language Model AI and Human-Generated Coaching Messages for Behavioral Weight Loss’, “Artificial Intelligence (AI) systems, particularly large language models (LLMs), can understand and generate natural language through machine learning, transcending the constraints of rule-based systems.” This capability enables attackers to craft highly convincing phishing messages that appear authentic and contextually relevant.

Automation at scale

Unlike traditional attacks that require significant manual effort, AI enables attackers to automate and deploy thousands of attacks simultaneously, often with each one tailored to a specific target.

According to CNCSO, “The average number of cyberattacks experienced by organizations per week has doubled over the past four years, from 818 in the second quarter of 2021 to 1,984 during the same period in 2025, an increase of 58% in two years. This accelerating trend is a direct reflection of the widespread use of AI technologies in the attack chain.” The article further notes that attackers now use AI to generate phishing messages at scale, clone voices, and automate intrusion processes.

Continuous Learning

One of the most concerning aspects of malicious AI is its ability to learn and improve over time. Unlike static attack methods, AI-driven systems can adapt based on feedback, making each attack more effective than the last. In the article ‘AI is reshaping cybercrime: faster, automated and harder-to-detect attacks,’ the authors show that machine learning enables attackers to identify vulnerabilities, refine their techniques, and optimize exploitation strategies in real time. For example, AI can analyze which phishing emails are successful and adjust future messages to increase success rates.

New attack surfaces

AI itself is now a target. Attackers exploit AI systems through prompt injection, data poisoning, and model manipulation. For instance, attackers can inject malicious inputs into AI systems to override their intended behavior or extract sensitive information. A recent report found that 89% of organizations detected risky AI prompts in 2025, with a significant increase in high-risk prompt-based attacks.

See also: Zero trust and artificial intelligence security in healthcare

Types of malicious AI to watch for

Malicious AI doesn’t exist in just one form, and according to IBM, the risks of AI span multiple domains, many of which can be directly exploited by bad actors. Understanding these categories helps you recognize how AI can be misused in real-world scenarios.

AI-driven cyberattacks

Attackers can exploit AI systems to generate convincing phishing emails, clone voices, and create fake identities at scale. IBM notes that threat actors are already using AI tools to “scam, hack, steal a person’s identity or compromise their privacy and security.” This type of malicious AI is particularly dangerous because it combines automation with realism. AI can craft messages that are not only grammatically perfect but also contextually relevant, making them far more difficult to detect than traditional scams.

Misinformation and deepfakes

AI has become a powerful tool for spreading misinformation and manipulating public perception. From deepfake videos to AI-generated news content, malicious actors can create highly realistic but false information at scale.

IBM indicates that AI can generate content that influences people’s decisions and actions, including impersonations of public figures or fabricated events.

Deepfakes, in particular, pose a growing threat. These AI-generated images, audio, or videos can misrepresent individuals, damage reputations, or even be used for fraud and extortion. As these technologies improve, distinguishing between real and fake content becomes increasingly difficult.

Data privacy exploitation

Many AI systems rely on vast amounts of data, often collected from users without explicit awareness or consent. This creates opportunities for malicious exploitation.

According to IBM, AI models may be trained on data scraped from the internet, including personally identifiable information (PII). In the wrong hands, this data can be used to:

• Build highly targeted phishing campaigns
• Conduct identity theft
• Profile individuals for manipulation

Adversarial attacks and model manipulation

Attackers can manipulate AI models to behave incorrectly or reveal sensitive information. IBM identifies several key techniques used in these attacks, including:

• Prompt injection: Feeding malicious inputs to trick AI systems into bypassing safeguards
• Adversarial inputs: Manipulating data to cause incorrect outputs
• Model tampering: Altering how an AI system functions

These attacks are especially concerning because they exploit the AI system’s own logic, making them difficult to detect using traditional security measures.

AI bias exploitation

Bias in AI systems is often seen as an ethical issue, but it can also be weaponized. If an AI system is trained on biased or incomplete data, attackers can exploit those weaknesses to influence outcomes. IBM explains that AI can inherit biases from training data, leading to skewed or discriminatory results.

In a malicious context, this could mean:

• Manipulating hiring systems
• Exploiting financial decision models
• Targeting vulnerable populations

This type of malicious AI is subtle but impactful, as it can reinforce harmful patterns without being immediately obvious.

AI-powered surveillance and privacy violations

AI can also be used for intrusive surveillance and tracking. When combined with facial recognition, behavioral analysis, or large-scale data collection, AI can monitor individuals in ways that raise serious privacy concerns.

IBM warns that AI systems handling personal data can expose users to privacy breaches if not properly secured.

AI hallucinations

Not all malicious AI is intentionally harmful; sometimes the danger lies in AI generating incorrect but believable information. IBM notes that AI systems can produce “inaccurate yet plausible outputs,” often referred to as hallucinations.

When exploited, these hallucinations can:

• Spread misinformation
• Mislead decision-making

How to identify malicious AI

Identifying malicious AI is about recognizing subtle warning signs in how content is generated and how systems behave. Messages that feel unnaturally perfect, with flawless grammar but lacking human nuance, can signal AI involvement. Similarly, overly personalized communication that references your job, location, or contacts may indicate AI-driven phishing, especially if the details feel slightly off.

Emotional manipulation is another common tactic. Messages that create urgency or fear should be treated with caution, even if they appear legitimate.

When it comes to media, deepfakes may reveal themselves through small inconsistencies like unnatural facial movements, audio delays, or visual distortions.

Furthermore, repeated messages, identical patterns, or instant, highly detailed responses may indicate automated activity. Finally, any request for sensitive information, such as passwords, personal identification, or financial data, should raise immediate concern, as legitimate AI tools do not require this level of access.

How to protect against malicious AI

To defend against malicious AI, the following best practices can be implemented:

• Verify before you trust: Always double-check email senders, links, attachments, and requests for sensitive information
• Use multifactor authentication (MFA): Even if your credentials are compromised, MFA adds an extra layer of protection.
• Limit data sharing: Avoid sharing sensitive information with AI tools unless necessary.
• Keep systems updated: AI-driven attacks often exploit vulnerabilities. Regular updates reduce risk.

See also: HIPAA Compliant Email: The Definitive Guide (2026 Update)

FAQS

How do attackers get the data used in AI-driven attacks?

Attackers often gather data from public sources, data breaches, or leaked databases. This information is then used by AI systems to generate more convincing and personalized attacks.

What should I do if I suspect malicious AI activity?

Do not engage with the content or provide any personal information. Verify the source through trusted channels, report the activity if possible, and use security measures such as multi-factor authentication to protect your accounts.