Artificial intelligence (AI) is becoming an increasingly common addition to everything from internal enterprise systems to customer-facing applications. While AI has the potential to reduce errors, save time, and cut costs, this technology isn’t without its drawbacks. 

Many organizations are deploying powerful models before fully understanding the security threats they introduce. From data poisoning to deepfakes, the vulnerabilities are not only technical—they’re ethical, legal, and operational. 

While organizations shouldn’t fear integrating AI into their systems, they do need a new approach to cybersecurity that addresses AI security concerns. 

In this guide, you’ll learn why AI security vulnerabilities are such a big concern for organizations, as well as tips for mitigating the top ten AI security risks.

Why AI Security Threats Are on the Rise

From generative models to machine learning-powered automation, AI systems' growing complexity and influence have opened new doors for cyberattacks. While organizations may have been able to avoid specialized approaches to AI protections in the past, that’s no longer the case. Several factors require a more focused approach to AI security risks, including: 

• Rapid adoption: Companies are integrating AI into critical systems faster than they can secure it. This creates vulnerabilities where models are launched without thorough testing, monitoring, or protective measures. It’s no wonder that 87% of cybersecurity professionals encountered AI-driven attacks in the last year. 
• Unique attack surfaces: Unlike traditional software, AI systems can be compromised in novel ways, including model inversion (revealing sensitive training data), data poisoning (corrupting model behavior), prompt injection (manipulating outputs), and adversarial attacks (feeding inputs designed to deceive the model). 
• AI weaponization: Generative AI makes it easier for bad actors to create convincing phishing emails, deepfakes, and malware. It enables faster, more scalable attacks that require minimal technical skill. To make matters worse, 47% of organizations say this technology is improving adversarial abilities, making attacks more common and harmful than ever before. 
• Increasing costs: The cost of a single cybersecurity breach is on the rise. In fact, ransomware costs are expected to reach $57 billion in 2025, nearly triple the estimated $20 billion cost in 2021. 

Solutions for the Top 10 AI Security Risks

Seventy-four percent of cybersecurity pros say AI security risks are a major challenge for their organizations. Designing a perfectly secure AI solution is impossible, but organizations can take common-sense approaches to mitigate the harms of AI security risks. 

Here, we’ll explore the top ten AI security risks facing businesses today—and what you can do to stop them in their tracks. 

1. Data Poisoning Attacks

With data poisoning, an attacker injects malicious data into your training data, which corrupts the model during learning. Poisoned data can lead to inaccurate predictions, systemic biases, or even model backdoors that remain hidden until triggered post-deployment.

You can defend against data poisoning by: 

• Securing AI data: Only use data from trusted, validated providers. It’s best to build your models on curated datasets, not scraped or aggregated content from unknown sources. Establishing a pipeline of clean, transparent data is one of the most effective long-term defenses.
• Auditing data: Use automated tools to review training data for anomalies or adversarial patterns. Data versioning is also helpful in tracking changes to training sets over time, which can provide auditable data for identifying potential tampering. 
• Using differential privacy or federated learning: These techniques minimize the risk of any single data point affecting the model’s overall behavior.

2. Model Inversion

In a model inversion attack, adversaries use the model’s outputs to reverse-engineer sensitive information from its training data, which could potentially expose private user data. This technique can lead to serious privacy violations, especially with health, financial, or biometric data. 

In some cases, attackers have been able to reconstruct facial images, health conditions, or transaction histories with model inversion attacks.

Defend against this AI security risk with: 

• Differential privacy: This approach adds statistical noise during training to obscure the contribution of any single data point, making it harder to extract specific records.
• Access limitations: Don’t allow just any user to access your model. Use rate limiting, authentication, and role-based access control (RBAC) for APIs and endpoints.
• Monitoring: Anomalous query patterns, like repeated probing, are a red flag that could indicate an inversion attempt. Set up 24/7 monitoring to spot and stop these attempts before they crack your model.

3. Prompt Injection Attacks

In prompt injection attacks, malicious users embed harmful instructions within input prompts to manipulate GenAI behavior. These attacks can lead to unauthorized actions, data leaks, or bypassing of safety protocols. 

For example, attackers might trick a model into revealing confidential information or generating malicious content by crafting a cleverly worded input.

Prevent prompt injection attacks with: 

• A zero-trust approach: Treat prompt injection like traditional command injection in software security: always validate, never blindly trust, and apply layered defenses.
• Context separation: When possible, isolate user instructions from system-level prompts to avoid unintended behavior crossover.
• Input validation: Sanitize and restrict user inputs to prevent injection of commands or prompt-breaking tokens.
• Filters: Use both rule-based and AI-powered content filters to detect and block unsafe, manipulative, or policy-violating inputs.

4. Model Theft and IP Leakage

Also known as model extraction, model theft happens when attackers repeatedly query an AI model’s API to replicate its functionality, effectively stealing the model without accessing the source code or training data. Academic research has shown that with enough queries, attackers can reproduce a model’s decision boundaries—even when its architecture is unknown.

Once an attacker steals the model, they can redistribute, misuse, or incorporate it into a competing offering. While you don’t technically lose data with this AI security risk, you do lose valuable intellectual property. 

Just as you would protect software source code or customer databases, your AI model deserves enterprise-grade IP protection. Combine technical controls with legal safeguards (e.g., usage terms, model licenses) to reduce exposure. You can also prevent model theft through: 

• Watermarking: Embed invisible signals in model outputs that can help detect copycats.
• Anomaly detection: Monitor query patterns to flag behavior consistent with reverse engineering. For example, the system will flag repetitive, uniform inputs that could indicate an attempt at model theft. 
• Rate limiting: Restrict the number of queries allowed per user or IP address to slow down extraction attempts.

5. Evasion Attacks

Evasion attacks (also known as adversarial examples) involve making subtle, often imperceptible changes to input data that cause a model to produce incorrect outputs. These are especially common in image recognition and computer vision systems. 

In high-stakes applications like autonomous vehicles or facial recognition, evasion attacks can have dangerous real-world consequences. For instance, researchers have shown that a few stickers placed on a stop sign can cause an AI system to misclassify it as a speed limit sign, potentially leading to accidents.

Defend against this AI security vulnerability through: 

• Adversarial stress-testing: Expose the model to adversarial examples during training so it learns to detect and resist them.
• Robust architecture: Use architectures that are more resilient to perturbations and include defenses like input normalization or feature squeezing.
• Anomaly detection: Monitor input data for statistical anomalies that might indicate an adversarial attack.

6. Lack of Model Transparency

Many AI systems operate as black boxes, making it difficult for developers or end users to understand how they make decisions. This lack of transparency can obscure biases, errors, or even deliberate manipulations within the model. 

Without visibility into model logic, holding systems accountable is nearly impossible, especially in high-risk domains like healthcare, finance, and criminal justice. A lack of explainability makes it harder to audit model behavior and detect unexpected or malicious changes—especially if a model starts making harmful decisions without an obvious cause.

Not all organizations can provide complete transparency because of IP concerns, but you can still balance IP protection and ethical use. Defend against a lack of transparency through: 

• Using explainable AI (XAI) tools, which break down predictions into understandable components. 
• Explainability frameworks. 
• Adopting hybrid models that balance performance with interpretability. 

It may seem counterintuitive, but a lack of transparency is a security issue. If you can’t audit your model’s reasoning, you can’t reliably detect manipulation or abuse. Prioritize explainability as a core security and ethical safeguard.

7. Supply Chain Vulnerabilities

It’s tough to create an entirely internally designed AI solution. Most organizations rely on external providers, datasets, and models to speed up development. 

Unfortunately, many AI systems depend on third-party models, libraries, datasets, or APIs—any of which can be compromised. When organizations unknowingly integrate compromised components, they expose themselves to hidden backdoors, malware, or vulnerabilities buried deep in the AI stack. 

These shadow dependencies can quietly introduce serious security risks. For example, a backdoored model from a public repository might behave normally under most conditions but activate malicious behavior in specific contexts, making detection incredibly difficult.

Mitigate supply chain vulnerabilities by: 

• Using validated models, libraries, and datasets from verified sources. 
• Documenting all data origins and changes. 
• Automatically scanning for outdated or vulnerable components. 
• Patching regularly. 

8. Insecure APIs and Integration Points

Many AI systems expose sensitive API data to query models, retrieve data, or integrate with other platforms. This step is necessary to improve AI performance, but left unguarded, APIs can become a prime attack surface. 

Insecure APIs can lead to unauthorized access, data scraping, prompt injection, or even full model theft. The risk increases when AI tools are hastily integrated into broader systems without standardized security practices, especially in cloud environments or multi-tenant platforms.

In GenAI systems, APIs may inadvertently expose sensitive prompt contexts or return verbose outputs that leak internal logic. Securing these interfaces requires both traditional API hardening and GenAI-specific sanitization.

System integration points often introduce vulnerabilities that are easy to overlook but simple to exploit. Lock down your APIs with the same rigor you'd apply to your core infrastructure. Stop this AI security risk by: 

• Securing API gateways: Use gateways to enforce throttling, logging, IP restrictions, and protocol-level security. Authentication protocols like OAuth 2.0, tokens, and mutual TLS also protect APIs.
• Limiting access: Ensure only authorized users can access specific API functionality based on their role.
• Monitoring activity: Set up alerts for suspicious query patterns, unusual volumes, or repeated failed access attempts.

9. Impersonation and Deepfakes

Have you ever viewed a photo or video online and thought, “There’s no way this is real”? Chances are, you’re looking at a deepfake. 

Generative AI can now convincingly mimic voices, faces, and writing styles, leading to deepfakes and impersonation attacks that deceive humans and machines alike. These attacks can spoof identities, forge documents, or manipulate media.

Deepfakes and synthetic content pose serious threats to trust and security. Attackers have used voice cloning to trick executives into transferring funds, and fake videos to spread misinformation or damage reputations. Fortunately, you can fight against this AI security risk by: 

• Authenticating content: Use digital watermarking, provenance metadata, and standards like C2PA to verify authenticity.
• Using deepfake detection tools: Continuously update your detection models to flag synthetic or manipulated content.
• Training your team: Don’t let employees fall for deepfakes. Educate staff on the risks and signs of AI-generated phishing attempts, audio fraud, or manipulated documents.
• Setting up multi-factor authentication: Don’t rely solely on voice, video, or written requests. Always layer identity validation, especially in high-risk industries like healthcare and finance. 

10. Poor Governance and Lack of AI Policy

Many organizations deploy AI without a formal governance structure, which means they have no clear policies, oversight mechanisms, or accountability for how models are trained, used, or monitored. Without AI governance, it’s easy for teams to unintentionally violate ethical standards, compliance requirements, or even introduce serious vulnerabilities. 

According to the Darktrace 2025 report, while CISO confidence in defending AI threats is rising, only 42% of cybersecurity professionals fully understand the AI systems in their stack. That gap starts with governance. You can’t secure what you don’t see—and you can’t trust what you don’t control.

Investing in proper policy and training is the best way to mitigate this AI security risk. Develop a formal AI governance policy to define who can guide and use AI, under what conditions, and with what data. 

Tools like model cards and SBOMs for AI can support visibility, accountability, and compliance in complex AI systems.

Train staff on responsible use, which can also help promote a culture of ethical awareness and data privacy. Once you have several AI models in play, ask your team to document which are in use, who owns them, and how they maintain them.

 For help building internal expertise, check out these AI security training courses and resources designed to upskill your team and support a culture of safe, informed AI use.

The table below breaks down the top 10 AI security risks and key strategies to protect your systems.  

Smarter AI Demands Smarter Security

From data poisoning and model theft to deepfakes and governance gaps, the rise of AI security threats is both broad and fast-moving. Each of the ten vulnerabilities we’ve covered highlights a critical blind spot that organizations can no longer ignore.

While AI introduces new risks, it also brings the tools to defend against them, if implemented responsibly. Organizations can harness AI safely and sustainably with the right mix of governance, transparency, and proactive defense.

Still, many organizations lack the time and expertise to mitigate AI security risks. That’s where Mindgard’s Offensive Security solution comes in. 

We help organizations stay ahead of evolving AI threats with cutting-edge AI red teaming and risk assessment tools. From stress-testing your models to identifying vulnerabilities, our team brings visibility and resilience to your AI stack. 

To better understand the breadth of potential vulnerabilities, security leaders can explore resources like the MIT AI Risk Repository—a curated database of real-world attack scenarios and red teaming insights for AI systems.

Book a Mindgard demo today to secure your AI systems against sophisticated attacks. 

Frequently Asked Questions

What is the biggest AI security threat today?

Data poisoning and prompt injection are currently the most pressing AI and LLM security risks. Both can silently compromise a model’s behavior, leading to inaccurate outputs, privacy breaches, or unintended misuse. As generative AI becomes more widespread, threats like deepfake impersonation and model theft are also gaining urgency.

How can organizations start securing their AI systems?

Start by identifying all AI assets across your organization, including models, datasets, APIs, and third-party tools. Implement security best practices like access control, data validation, and API monitoring. Then, build a formal AI governance policy covering everything from ethical use to compliance and supply chain management.

Can AI help defend against AI-related threats?

Yes. AI-powered security tools can detect anomalies, flag adversarial activity, and automate threat response faster than traditional methods. Solutions like AI red teaming from offensive security service providers like Mindgard simulate attacks on your models to uncover vulnerabilities before real attackers can exploit them.