The 10 Biggest AI Security Threats, Ranked

Introduction: Knowing Your Enemy

In cybersecurity, awareness is the first step to defense. You can’t protect your AI if you don’t know what you’re protecting it from.

That’s why today we’re ranking the 10 biggest AI security threats of 2025, based on:

• OWASP LLM Top 10
• MITRE ATLAS
• Real-world attack data from PointGuard AI and industry reportsPointGuard - Six Steps …

This list is designed to help CISOs, CTOs, and AI leaders prioritize resources where they matter most.

Ready to protect your AI stack from prompt injection, data leakage, and more? Click Here

The Threat Ranking

1. Prompt Injection (Easy, High Impact)State-of-LLM-Applicatio…

Attackers embed malicious instructions in user input to bypass safety controls.

• Example: A chatbot tricked into revealing sensitive banking data.
• Defense: Input sanitization, output filtering, runtime monitoring.

2. Data Leakage / Sensitive Information Disclosure (Easy, High Impact)

AI outputs unintended sensitive data from training sets.

• Example: An AI inadvertently reveals PII from its dataset.
• Defense: Access controls, data masking, strict output filtering.

3. Data Poisoning (Medium, High Impact)

Malicious data inserted into training sets to skew AI behavior.

• Example: Fraud detection model trained to ignore certain scams.
• Defense: Data provenance checks, anomaly detection, secure pipelines.

4. AI Supply Chain Exploits (Easy, High Impact)

Third-party models or libraries containing hidden backdoors.

• Example: Compromised open-source model disables safety systems.
• Defense: AI-BOM tracking, model scanning, vendor audits.

5. Model Evasion / Jailbreaking (Medium, High Impact)

Manipulating inputs to bypass AI’s guardrails.

• Example: Content filters disabled via adversarial prompts.
• Defense: Adversarial training, sandboxing, runtime defenses.

6. Model Theft / Extraction (Medium, Medium Impact)

Reverse-engineering deployed AI to steal algorithms.

• Example: Competitor clones your proprietary model.
• Defense: Rate limiting, output watermarking, secure hosting.

7. Misconfigured Access Controls (Easy, Medium Impact)

Over-permissive roles in AI environments.

• Example: Exposed S3 bucket containing training data.
• Defense: Least privilege, configuration monitoring.

8. Adversarial Inputs (Medium, Medium Impact)

Inputs crafted to produce incorrect outputs.

• Example: Image recognition fooled by altered pixels.
• Defense: Model hardening, adversarial detection tools.

9. Insecure Integration Points / APIs (Medium, Medium Impact)

Weaknesses in APIs connecting AI systems.

• Example: API hijacking to send malicious inputs.
• Defense: API security scanning, token-based authentication.

10. Model Denial of Service (Medium, Low Impact)

Overloading AI with complex requests to disrupt service.

• Example: LLM API overwhelmed by high-complexity prompts.
• Defense: Rate limiting, infrastructure scaling, anomaly detection.

 Do you know where your AI stands against these 10 threats? Fill out the form to get your free AI risk readiness checklist
 

How PointGuard AI Tackles All 10 Threats

• Pre-deployment Red Teaming: Simulates attacks from 1–10.
• Runtime AI Defense: Real-time detection and blocking.

• Full-Stack Protection: Covers model, infrastructure, and supply chain.

   

Want to know if your AI deployments can withstand 2025’s top threats? Book a Free consultation with FSD Tech’s AI security experts. Schedule your session today.

FAQ

Q1: What are AI security threats?

AI security threats are ways attackers can misuse or damage AI systems — from stealing sensitive data to tricking AI into making wrong decisions. Just like you lock your doors to protect your office, you need safeguards to protect your AI.

Q2: Why should businesses in GCC and UAE care about AI security?

In GCC and UAE, AI is being used in banking, oil & gas, healthcare, retail, and government. If AI is hacked or tricked:

• Customer trust can be lost.
• Regulators may impose fines.
• Operations can be disrupted, costing millions.

Q3: What is a “Prompt Injection” attack?

A prompt injection is when someone tricks an AI with sneaky instructions so it ignores safety rules.

Example: A chatbot designed for banking is tricked into revealing account details.

Prevention: Filter inputs, monitor conversations in real time, and block unsafe responses.

Q4: How does “Data Leakage” happen in AI?

Data leakage happens when AI accidentally reveals sensitive information it learned from its training data.

Example: An AI reveals someone’s private health records in its answer.

Prevention: Use data masking, control who can access the model, and strictly filter outputs.

Q5: What is “Data Poisoning” in AI?

Data poisoning is when bad data is added to AI’s training set so it learns the wrong things.

Example: A fraud detection AI is trained to ignore certain types of scams.

Prevention: Check where data comes from (data provenance) and watch for unusual results.

Q6: What are “AI Supply Chain Exploits”?

This happens when a third-party AI tool or library you use has hidden malicious code.

Example: A free AI model downloaded from the internet disables your safety systems.

Prevention: Keep a full list of all AI components (AI-BOM), scan them for threats, and only use verified vendors.

Q7: What is “Model Evasion” or “Jailbreaking”?

Model evasion is when someone finds a way around AI’s restrictions to make it do something it shouldn’t.

Example: An AI filter meant to block harmful content is bypassed using clever prompts.

Prevention: Train AI to handle adversarial inputs and test it with attack simulations.

Q8: What is “Model Theft” or “Model Extraction”?

This is when attackers steal your AI’s algorithm by testing it repeatedly until they can copy it.

Example: A competitor replicates your AI-powered service without investing in R&D.

Prevention: Limit how often people can query your AI, add invisible watermarks to outputs, and host models securely.

Q9: How do “Misconfigured Access Controls” cause AI risks?

If the wrong people have access to AI systems, they can change settings or steal data.

Example: A public cloud storage bucket with AI training data left unprotected.

Prevention: Apply least privilege access, monitor settings, and review permissions regularly.

Q10: What are “Adversarial Inputs” in AI?

These are carefully crafted inputs designed to fool AI.

Example: An image recognition AI misidentifies an object because tiny pixels were changed.

Prevention: Harden the AI model and use tools that detect such manipulations.

Q11: What are “Insecure Integration Points” or APIs?

AI often connects to other systems through APIs (Application Programming Interfaces). If these are weak, attackers can send harmful data to AI.

Example: Hackers send fake requests through an unsecured API to confuse the AI.

Prevention: Use API authentication, security scanning, and encryption.

Q12: What is an AI “Denial of Service” attack?

This is when AI is overloaded with requests so it slows down or stops working.

Example: AI chatbot made unusable by sending thousands of complex prompts at once.

Prevention: Limit requests per user and scale infrastructure to handle spikes.

Q13: Which AI threat is the most dangerous?

In 2025, Prompt Injection and Data Leakage are ranked as the most dangerous because they are:

• Easy for attackers to try.
• Capable of causing huge damage quickly.

Q14: How can companies defend against all 10 threats?

By combining:

1. Pre-deployment testing (simulate attacks before launch).
2. Runtime monitoring (watch AI in real time).
3. Supply chain checks (verify third-party tools).
4. Access controls (limit permissions).

Q15: How does PointGuard AI protect against these threats?

PointGuard AI:

• Runs AI Red Teaming to test defenses before launch.
• Monitors for real-time threats like prompt injection or API abuse.
• Secures the entire AI stack, from models to APIs to supply chains.