Top 7 AI Security Threats in 2025: What Every Business Must Know

By Patrick D. Dasoberi | CISA, CDPSE, MSc IT | Former CTO, CarePoint

Here's a number that should keep you up at night: 93% of security leaders are bracing for daily AI-powered attacks in 2026.

This isn't fear-mongering. It's reality.

According to the World Economic Forum's Global Cybersecurity Outlook 2025, 66% of organizations believe AI will have the most significant impact on cybersecurity this year. And they're right—but not always in the way they expect.

I spent four years as CTO at CarePoint, securing over 25 million patient records across Ghana, Nigeria, Kenya, and Egypt. During that time, I watched AI transform from a promising tool into both our greatest asset and our most unpredictable threat vector.

The attacks I'm seeing now are different. They're faster, more sophisticated, and exploit vulnerabilities that didn't exist two years ago.

In this comprehensive guide, I'll walk you through the seven most critical AI security threats that will be dominating 2026—and more importantly, what you can actually do about them. 
Let's get into it.

Prompt injection is the SQL injection of the AI era.

It exploits a fundamental weakness in how Large Language Models (LLMs) work: they can't reliably distinguish between instructions and data. When you feed an LLM a document, email, or webpage containing hidden instructions, it may execute those instructions as commands.

This isn't a bug that can be patched. It's an architectural limitation baked into how these systems process language.

Real-World Examples from 2025
This year, security researchers demonstrated prompt injection attacks against virtually every major AI platform:

• GitHub Copilot Chat—Researchers extracted sensitive data from private repositories (CSO Online)
• GitLab Duo—Hidden prompts in code comments triggered unauthorized actions (CSO Online)
• ChatGPT, Claude, Gemini—All vulnerable to indirect injection via external content
• Microsoft Copilot Studio—Zero-click attacks demonstrated at Black Hat 2025 (CSO Online)
• Salesforce Einstein—Enterprise data exfiltration through crafted inputs
• AI-enabled browsers (Perplexity Comet, Copilot for Edge, Gemini for Chrome)—Malicious prompts hidden in URL fragments (CSO Online)

The attack surface is massive and growing.

How to Defend Against Prompt Injection

There's no silver bullet, but layered defences help:

1. Context separation—Run different tasks in isolated LLM instances
2. Least privilege—Limit what actions your AI agents can perform
3. Human-in-the-loop—Require approval for sensitive operations
4. Input filtering—Screen for common injection patterns
5. System prompt hardening—Instruct LLMs to ignore commands in ingested data
6. Structured data formats—Use JSON schemas to separate instructions from content

Learn more about AI attack vectors in our AI Cybersecurity Fundamentals guide →

Remember SolarWinds? AI supply chain attacks are the 2025 equivalent—but potentially worse.

This year, security researchers from ReversingLabs discovered malware hidden inside AI models hosted on Hugging Face, the largest repository for open-source machine learning assets. Separately, they found trojanized packages on PyPI posing as legitimate SDKs for Alibaba Cloud's AI services.

The attack vector? The Pickle serialization format.

Pickle is how Python stores AI models for use with PyTorch, one of the most popular machine learning frameworks. The problem is that Pickle files can execute arbitrary code when loaded. Attackers are exploiting this to hide malware inside seemingly legitimate models.

Why This Matters
When your developers download a pre-trained model to fine-tune for your use case, they might be importing:

• Backdoors that activate under specific conditions
• Data exfiltration code that phones home
• Credential stealers targeting your cloud infrastructure
• Ransomware payloads waiting to deploy

The model works perfectly—until it doesn't.

My Recommended Mitigation Strategies

1. Verify model sources—only download from verified publishers
2. Use model signing—implement cryptographic verification
3. Scan before deployment — Use tools that detect malicious serialised objects
4. Isolate model loading — Run imports in sandboxed environments
5. Monitor model behavior — Watch for unexpected network calls or file access

Dive deeper into AI risk assessment in our AI Risk Management framework. →

LLMjacking is credential theft specifically targeting AI services—and it's now so prevalent it has its own name.

Attackers steal API keys for services like OpenAI, Anthropic, Amazon Bedrock, and Azure OpenAI. Then they either use the access themselves or resell it on dark web marketplaces.

In 2025, Microsoft filed a civil lawsuit against a gang specialising in LLM credential theft. The group was stealing access, bypassing ethical safeguards, and selling AI-generated content services to other criminals.

The Financial Impact

LLMjacking doesn't just compromise security—it destroys budgets.

Large-scale API abuse against cutting-edge models can generate costs exceeding $100,000 per day for the credential owner. Victims often don't discover the theft until they receive astronomical cloud bills.

How Attackers Get Your Credentials

• Credential stuffing—Testing stolen username/password combinations
• Infostealer malware—Harvesting API keys from developer machines
• Repository scanning—Finding keys accidentally committed to GitHub
• Phishing—Targeting developers with fake AI service login pages

Protection Measures

1. Never hardcode API keys — Use environment variables and secrets managers
2. Implement usage alerts — Set up cost anomaly detection
3. Rotate credentials regularly — Especially after employee departures
4. Use IP restrictions — Limit API access to known addresses
5. Enable MFA everywhere — Particularly for AI service accounts

Explore credentials from AiSecurityinfo website →

The Unsanctioned AI Explosion
Here's a statistic that should concern every CISO: 49% of employees use AI tools not approved by their employers.

Even more alarming? Over half of those users don't understand how their inputs are stored, processed, or potentially used to train future models.

Shadow AI is shadow IT on steroids. When employees paste confidential data into ChatGPT, upload proprietary documents to Claude, or use random AI tools to "just get things done," they're creating data leakage vectors your security team can't see.

The Compliance Nightmare
Shadow AI doesn't just create security risks—it creates regulatory exposure.

If an employee pastes customer PII into an unsanctioned AI tool:

• GDPR violation — Unauthorized data transfer to third parties
• HIPAA violation — PHI disclosed without proper safeguards
• Industry regulations — Financial, legal, and healthcare all have specific requirements

According to Check Point Research, 1 in every 80 GenAI prompts poses a high risk of sensitive data leakage. That's not a theoretical risk—it's happening in your organisation right now.

How to Address Shadow AI

1. Create clear AI usage policies — Define what's allowed and what isn't
2. Provide approved alternatives — If you don't offer sanctioned tools, people will find their own
3. Implement DLP for AI — Monitor for sensitive data leaving to AI services
4. Train employees — Most shadow AI use comes from ignorance, not malice
5. Regular audits — Discover what AI tools are actually in use

Build your AI governance program with our Enterprise AI GRC guide →

What Is MCP and Why Does It Matter?

The Model Context Protocol (MCP) has become the standard for connecting LLMs to external data sources and applications. It's how AI agents access tools, databases, and APIs to actually do useful work.

MCP adoption has exploded in 2025, with tens of thousands of MCP servers now published online. Popular development environments like VS Code, Cursor, and Claude Code CLI all support MCP integration natively.

The problem? Many of these servers are misconfigured, vulnerable, or outright malicious.

Real Vulnerabilities Discovered in 2025

Security researchers demonstrated alarming attacks this year:

• Rogue MCP servers injecting malicious code into Cursor IDE's built-in browser
• Prompt hijacking attacks exploiting weak session ID generation
• OS command injection through misconfigured server parameters
• Over 200 completely unprotected Chroma vector database servers exposed to the internet (Trend Micro)

MCP servers can be downloaded from anywhere—GitHub, random websites, package managers. They can contain malicious code that runs with whatever permissions your AI agent has.

Securing Your MCP Infrastructure

1. Vet every MCP server — Only use servers from trusted sources
2. Run in isolation — Sandbox MCP servers from critical systems
3. Audit configurations — Check for command injection vulnerabilities
4. Secure communications — Implement proper authentication and encryption
5. Monitor behavior — Watch for unexpected actions from MCP-connected agents

Learn about AI security tools in our AI Security Tools guide →

The Evolution of Social Engineering

Deepfakes have moved from novelty to weapon.

In 2025, we're seeing autonomous, interactive deepfakes that can hold real-time conversations. These aren't pre-recorded videos—they're AI systems that can respond dynamically, making detection nearly impossible through traditional means.

A recent FBI alert highlighted the surge in AI-generated content for fraud. The threat became tangible when attackers used AI-generated audio to impersonate Italy's defense minister, causing significant financial harm (Check Point Research).

Why Traditional Verification Is Failing

The attacks are breaking our fundamental assumptions about identity:

• Voice verification — AI can clone voices from minutes of sample audio
• Video calls — Real-time deepfakes can fool visual inspection
• Written communication — LLMs generate perfect, personalized phishing content
• Multi-channel attacks — Combining voice, video, and text for coordinated deception

When you can't trust what you see, hear, or read, how do you verify identity?

Building Deepfake-Resistant Processes

1. Multi-factor authentication — Beyond just something you know
2. Out-of-band verification — Confirm requests through separate channels
3. Code words — Establish secret phrases for high-value transactions
4. Behavioral analysis — Look for patterns that don't match known behavior
5. AI detection tools — Deploy systems that identify synthetic content

Explore data protection strategies in our Data Privacy & AI guide →

The State of AI Tool Security
According to Orca Security's 2025 State of Cloud Security report, 84% of organisations now use AI-related tools in the cloud. But here's the kicker: 62% have at least one vulnerable AI package in their environments.

The Cloud Security Alliance adds another sobering data point: one-third of organisations experienced a cloud data breach involving an AI workload in the past year. The causes?

• 21% from vulnerabilities
• 16% from misconfigured security settings
• 15% from compromised credentials or weak authentication

Even tools from major vendors aren't immune:

Langflow (AI agent framework)

OpenAI Codex CLI

NVIDIA Triton Inference Server

Meta, Microsoft, NVIDIA inference frameworks

Ray compute framework

vLLM, SGLang

Remote Code Execution

Remote Code Execution

Multiple vulnerabilities

Copy-paste RCE

Insecure deployment

Remote Code Execution

Critical — Actively exploited

High

High

Critical

High — Thousands hacked

High

1. Inventory all AI components — You can't secure what you don't know exists
2. Patch aggressively — AI tools are actively targeted
3. Security review before deployment — Involve your security team early
4. Network segmentation — Isolate AI workloads from critical systems
5. Continuous monitoring — Watch for exploitation attempts

Find secure AI tools in our AI Security Tools guide →

Protecting against AI threats requires a comprehensive approach. Here's the framework I developed while securing healthcare AI systems across four countries:

1. Establish AI Governance (Pillar 5)

• Create clear policies for AI use and development
• Define roles and responsibilities
• Build accountability structures

2. Master the Fundamentals (Pillar 1)

• Understand AI-specific threat models
• Train security teams on AI attack vectors
• Build incident response playbooks for AI systems

3. Implement Risk Management (Pillar 2)

• Assess AI systems for vulnerabilities
• Quantify and prioritize risks
• Create mitigation roadmaps

4. Ensure Regulatory Compliance (Pillar 3)

• Map requirements (EU AI Act, GDPR, industry regulations)
• Document AI systems and their data flows
• Prepare for audits

5. Protect Data Privacy (Pillar 4)

• Secure training data
• Monitor model inputs and outputs
• Implement data loss prevention for AI

6. Deploy Security Tools (Pillar 6)

• AI-specific security monitoring
• Vulnerability scanning for AI components
• Threat detection tuned for AI attacks

7. Apply Industry Standards (Pillar 7)

• Follow sector-specific guidance
• Implement relevant frameworks (NIST AI RMF, ISO 42001)
• Learn from industry incidents

Explore the complete 7-pillar framework →

93% of security leaders expect daily AI attacks

66% say AI will have biggest cybersecurity impact

84% of organizations use AI tools in the cloud

62% have vulnerable AI packages

49% of employees use unsanctioned AI

1 in 80 GenAI prompts risks data leakage

1 in 13 prompts contains sensitive information

33% had AI-related cloud breaches

90% of organizations not prepared for AI security

89% of AI usage invisible to security teams

$100,000+/day potential LLMjacking costs

8,000+ data breaches in H1 2025

World Economic Forum

WEF Global Cybersecurity Outlook 2025

Orca Security

Orca Security

2025 Employee Survey

Check Point Research

Check Point Research

Cloud Security Alliance

securityHelp Net

Enterprise Survey

Security Research

Experian

AI security isn't a future problem. It's a right-now problem.

The threats I've outlined aren't theoretical—they're actively being exploited against organisations worldwide. The question isn't whether your organisation will face AI-related security challenges, but whether you'll be prepared when they arrive.

Here's what I know from experience: the organisations that start building their AI security programs today will have a massive advantage over those that wait.

The good news? You don't have to figure this out alone.

I've distilled everything I learned securing AI systems across four African countries into a comprehensive framework. It's practical, tested, and designed for real-world implementation.

Your Next Steps
1. Start learning today—Explore the complete 7-pillar framework for free

2. Go deeper — Join the Foundation Training Program launching soon

3. Stay informed—Subscribe to get weekly AI security insights delivered to your inbox

The threats will keep evolving. Your defenses need to evolve faster.

Patrick D. Dasoberi is the founder of AI Security Info and a certified cybersecurity professional (CISA, CDPSE) specialising in AI risk management and compliance. As former CTO of CarePoint, he operated healthcare AI systems across multiple African countries. Patrick holds an MSc in Information Technology and has completed advanced training in AI/ML systems, bringing practical expertise to complex AI security challenges.