AI Vulnerabilities in Web Applications

AI technology is rapidly reshaping web applications, from customer support bots to intelligent fraud prevention systems. However, with this growth comes new and often overlooked security risks. Many businesses are integrating AI without fully addressing the unique vulnerabilities it introduces.

Common AI Weaknesses

☠️ Model Poisoning – Malicious users can manipulate machine learning models by feeding them crafted or misleading data, leading to faulty outputs.

👀 Information Exposure – Insecure AI responses may unintentionally reveal sensitive data or system information.

💥 Prompt Injection Attacks – AI systems based on Large Language Models (LLMs) are susceptible to specially designed prompts that manipulate system behaviour.

🔓 Insecure APIs – AI features are often exposed through APIs, making them prime targets for abuse, data exfiltration, and denial of service attacks.

💡 Bias and Trust Flaws – Models trained on skewed or incomplete data can make inaccurate or unsafe decisions, sometimes in ways that aren’t immediately obvious.

Key Takeaway

AI adds valuable capabilities to web applications but also expands the attack surface. Organisations must proactively identify and secure AI-specific vulnerabilities as part of their broader cybersecurity programme.

Interested in learning more about AI security? Visit www.fortiscyber.co.uk or contact us at enquiries@fortiscyber.co.uk.