Top 5 AI Security Risks in 2026

What if the very technology we count on to defend us becomes a threat? As we head into 2026, the dual role of artificial intelligence, as both defender and adversary, is no longer theoretical. A recent survey found that 63 % of cybersecurity professionals cite AI-driven social engineering as the top attack vector for the coming year. 

The urgency is real, and so is the need for clarity. In this article, we’ll walk through the Top 5 AI Security Risks of 2026, so you can spot emerging threats early, anchor your strategy in evidence, and lead with confidence. 

Top 5 AI Security Risks

1. Data Poisoning

Imagine teaching a student using a textbook filled with a few deliberate lies. Over time, those small distortions twist the student’s understanding until they’re confidently wrong. That’s data poisoning in the world of AI.

Attackers subtly feed corrupted data into the training pipeline, sometimes just a handful of falsified entries among millions. To the untrained eye, the dataset looks clean. But the poison seeps in, alters how the model interprets the world. 

An AI trained on tampered data might misclassify threats, overlook malicious behavior, or even make decisions that benefit the attacker’s objectives. The danger lies in its subtlety: poisoned data doesn’t break the system overnight; it erodes its judgment over time.

Since August 2024, Group-IB’s Threat Intelligence team has tracked this phenomenon in the wild through a campaign known as “ClickFix” (also called “ClearFix”), a technique spreading across multiple operations. Our researchers dissected its infection chains and variants, identifying how malicious data streams were being injected into systems to skew outputs. Using these insights, we built detection signatures that now identify ClickFix websites at scale, with thousands already catalogued in our database.

Read our in-depth guide on AI in Cybersecurity for more information.

2. Bias

Every algorithm carries the fingerprints of its creators and their blind spots. Bias in AI is about imperfect data shaping imperfect judgment. When AI systems learn from real-world datasets, they inherit the flaws, imbalances, and historical biases baked into those datasets. In cybersecurity, that can translate into very real risks.

For instance, a biased threat-detection model might prioritize certain attack patterns while overlooking new or region-specific behaviors because its training data came mostly from a single geography or industry. 

An email security AI could flag messages from certain languages or domains as suspicious more often, not because they are malicious, but because the model’s early examples overrepresented them in phishing datasets.

At Group-IB, we’ve seen how subtle bias in AI can distort situational awareness. When an AI model learns that a particular type of attack “looks familiar,” it can unintentionally miss the outlier that truly matters. Bias, in this sense, narrows vision when clarity is most needed.

To mitigate it, organizations must feed AI systems with diverse, verified, and representative threat data. Regular audits, adversarial testing, and cross-validation across global threat intelligence sources help ensure models see the full picture—not just the parts they were trained to expect.

3. Data Breaches

A data breach is like a cracked vault; it may look intact from the outside, but the damage inside can ripple across systems, partners, and customers before anyone notices. When AI systems are involved, that vault often holds more than personal data. 

It contains training datasets, proprietary algorithms, and sensitive operational intelligence. Once exposed, attackers can reverse-engineer models or repurpose stolen data to train their own malicious AI systems.

A single breach can disrupt detection pipelines, compromise model integrity, and erode the trust on which AI systems rely. Worse, the breach doesn’t stop with one company; if compromised datasets are shared with partners or posted to open repositories, the poisoned intelligence spreads like wildfire.

To prevent such incidents, organizations must treat AI training data as critical infrastructure, encrypt it, segment it, and monitor it continuously for anomalies.

4. Data Privacy Issues 

Every conversation with an AI model leaves a trace. The same technology that enables Large Language Models (LLMs) to generate fluent, context-rich responses is built on massive volumes of data, often including fragments of real human behavior, preferences, and, sometimes, personal information.

Group-IB’s research highlights just how exposed businesses can become when this data lifecycle isn’t tightly controlled. For instance, in the Hi-Tech Crime Trends 2023/2024 report, the company found more than 225,000 logs on the dark web containing compromised accounts for generative-AI tools like ChatGPT, many of which came from broader leaks rather than targeted breaches.

5. Adversarial Attacks

Adversarial attacks subtly manipulate inputs, sometimes changing only a few pixels, values, or words to trick AI systems into making the wrong decision.

In cybersecurity, that can mean an AI-powered phishing detector suddenly classifies a malicious email as safe, or a fraud detection model fails to flag a suspicious transaction. The input looks harmless to the human eye but is engineered to push the model off balance.

Researchers have observed similar tactics during malware evasion campaigns, where threat actors slightly alter URLs, script hashes, or request patterns to slip past detection systems trained on static indicators. These micro-changes, imperceptible at scale, can effectively “blind” even advanced AI filters that rely on pattern recognition.

Two common variants illustrate the risk:

• Gradient-based attacks exploit how models learn by adjusting parameters, allowing attackers to steer predictions toward false outcomes.
• Input manipulation attacks, which feed AI systems misleading or crafted data to reduce their resistance over time.
  

AI models must be tested against adversarial inputs before deployment, their decision boundaries continuously monitored, and detection pipelines diversified. At Group-IB, this principle underpins every intelligence model we build: if attackers are experimenting with your AI, you should be testing it twice as hard.

Group-IB AI Red Teaming: Turning Uncertainty into Preparedness

The new era of AI-driven innovation brings undeniable opportunity, but also unseen risk. From data poisoning to model extraction, the same intelligence that powers automation can be quietly weaponized against it. What defines resilience isn’t avoiding AI adoption, but adopting it safely, with the same discipline used to protect every other part of your infrastructure.

That’s where Group-IB’s AI Red Teaming comes in. Our experts challenge your AI systems the way a real adversary would, testing for prompt manipulation, adversarial input, supply-chain exposure, and data leakage, before attackers ever get the chance.

Connect with Group-IB’s experts to schedule an AI Red Teaming assessment and turn today’s uncertainty into tomorrow’s advantage.