AI Security Risks in 2025: 6 Threats & 6 Defensive Measures

What Are AI Security Risks?

AI security risks emerge when artificial intelligence systems are compromised, exploited, or misused in ways that lead to unfavorable outcomes. These risks stem from vulnerabilities in AI models, datasets, and operational environments. 

For example, poorly secured AI systems can be used to launch cyberattacks, while biased training data may cause these systems to make unethical or unsafe decisions. As AI becomes integral to day-to-day technologies, threats to its security grow increasingly critical. AI systems can amplify existing cybersecurity risks or create entirely novel ones. 

Adversaries may exploit weaknesses in AI models to manipulate outputs, extract sensitive data, or disrupt operations. Expanding AI adoption further increases the attack surface, making it imperative for organizations to recognize and mitigate AI-specific vulnerabilities to protect their assets and stakeholders.

This is part of a series of articles about LLM Security.

Key Drivers Behind AI Security Threats

Here are some of the main reasons for the increase in the prevalence and severity of security threats to AI systems.

Growth of Generative AI and Its Unintended Consequences

Generative AI has reshaped creative and industrial processes but brought significant risks. For instance, tools like deepfake generators can create hyper-realistic fake audio, video, or images, often used maliciously in scams or disinformation campaigns. Cybercriminals leverage generative AI to automate sophisticated attacks, such as crafting personalized phishing messages or breaking CAPTCHA verifications.

The unintended consequences of generative AI also affect data privacy. Language models trained on unregulated datasets can inadvertently store sensitive information in their outputs. In the wrong hands, this data can be extracted and weaponized.

Expansion of AI in Critical Infrastructure and Enterprise Systems

AI is increasingly applied in managing critical infrastructure like power grids, healthcare systems, and transportation. While this improves efficiency, it also exposes essential operations to new cybersecurity threats. Attackers targeting these systems could lead to widespread disruptions or even endanger lives.

Similarly, enterprises adopting AI tools for decision-making, customer service, or fraud detection face vulnerabilities such as data breaches or manipulative adversarial attacks. The more AI systems integrate with core business operations, the higher the stakes for potential exploitation. 

Lack of Standardized AI Governance Frameworks

Global adoption of AI outpaces the development of unified governance frameworks, leaving gaps in regulating its deployment and security. This absence of standards fosters inconsistent practices, particularly in securing training data, monitoring model performance, and ensuring ethical outputs. 

Without clear regulations, addressing AI-specific threats remains reactive rather than proactive. The lack of standardized frameworks can also hinder collaboration between industries and nations. A lack of consensus on AI risk management makes it harder to share best practices. 

Categories of AI-Specific Security Risks

Here’s a deeper look at some of the main risks associated with AI.

1. AI-Powered Cyberattacks: From Phishing to Deepfakes

AI-powered attacks represent a new level of sophistication in cybersecurity threats. Phishing scams, for example, can use AI to generate convincing, personalized messages that trick recipients into revealing sensitive information. Deepfake technologies are exploited to create counterfeit videos and audios, often deployed for blackmail, fraud, or political misinformation. 

These capabilities blur the line between authentic and artificial, making detection challenging. The automated nature of AI-powered cyberattacks also allows for scalability, enabling criminals to target multiple victims simultaneously with minimal effort. 

2. Adversarial Input Manipulation and Model Evasion

Adversarial attacks exploit vulnerabilities in AI models by feeding them subtly altered inputs crafted to produce incorrect outputs. For example, slightly altering an image might trick an AI-powered security system into misidentifying a threat. Such vulnerabilities can lead to severe consequences, especially in industries like autonomous vehicles or medical diagnostics.

Model evasion is another critical concern. Attackers can design inputs to avoid detection altogether, bypassing AI-based defenses. Addressing adversarial input manipulation requires thorough testing, defensive techniques, and model retraining to improve resistance to these threats. Organizations must also stay informed on evolving attack methods.

3. Data Poisoning and Training Set Vulnerabilities

Data poisoning occurs when attackers intentionally introduce malicious information into an AI system's training dataset. By manipulating the data used to train models, adversaries can skew system behavior, leading it to make flawed decisions. For example, poisoned datasets used in an autonomous car could prevent the recognition of certain traffic signs, causing accidents.

Training set vulnerabilities often stem from poor data hygiene or a lack of oversight. Open-source datasets, while convenient, may include inaccuracies or hidden adversarial elements. Organizations should implement rigorous validation processes for their training data and isolate models from untrusted sources to prevent data poisoning.

4. Model Inversion and Extraction Attacks

Model inversion techniques allow attackers to infer sensitive details about the original input data by interacting with an AI model. For example, an adversary targeting a medical diagnostic AI could reconstruct patient information based on queries and model behaviors. Such exposures pose significant risks to data privacy.

Model extraction attacks focus on replicating proprietary AI models by observing their outputs. This threatens intellectual property and enables malicious re-purposing of stolen algorithms. To mitigate these risks, strategies such as rate limiting, query monitoring, and encryption are required to protect model confidentiality.

5. AI Supply Chain Threats

AI supply chains encompass datasets, pre-trained models, and vendor-provided tools, all of which can introduce vulnerabilities. Compromises during development or distribution stages allow attackers to embed malicious elements that later exploit end-user systems. Supply chain threats are particularly problematic in unregulated development environments.

To secure the AI supply chain, organizations must verify the integrity of third-party software, enforce compliance standards, and conduct security audits. This ensures that dependencies are trustworthy and eliminates the risk of introducing backdoors into external technologies. Improved supply chain transparency is essential for mitigating threats.

6. Shadow AI and Unauthorized Model Deployment

Shadow AI refers to AI systems implemented without organizational approval or proper oversight. Employees or departments may deploy unvetted tools to solve immediate problems, but this practice bypasses essential security protocols. Unsupported or outdated AI models can introduce vulnerabilities, jeopardizing enterprise networks.

Unauthorized AI deployments also complicate compliance with data protection laws. These systems may process sensitive data without adequate safeguards, increasing the risk of breaches. Organizations must prioritize visibility into AI systems while creating policies that regulate deployment, thereby maintaining both security and compliance.

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6 AI Security Best Practices 

Here are some of the ways that organizations can help protect themselves against AI-related security threats.

1. Use Runtime Security to Continuously Discover AI Models in Use, Respond to New Vulnerabilities, and Protect Inference Servers

AI systems often run across distributed environments, including cloud platforms and edge devices, making it difficult to track all active models. Runtime security tools help identify deployed models in real time, enabling organizations to map their AI assets and uncover shadow AI deployments that may have bypassed governance protocols.

Beyond discovery, runtime protection involves detecting and mitigating vulnerabilities as they arise. This includes guarding inference servers against unauthorized access, abuse, or adversarial inputs. Automated threat response mechanisms can shut down compromised processes or isolate affected components, limiting damage from real-time attacks.

Effective runtime security also involves continuous monitoring of system behavior. Anomalous usage patterns, unusual resource consumption, or unexpected output changes can all signal a breach. Integrating runtime security with SIEM (Security Information and Event Management) tools provides security teams with a full-stack view for incident detection and response.

2. Limit Model Access to Authenticated Services Only

Limiting access to AI models ensures that only verified systems and users can interact with them. This restriction helps prevent unauthorized queries that might lead to data leakage, model inversion, or denial-of-service attacks. Every interaction should be authenticated using secure credentials, API tokens, or service meshes that support fine-grained identity management.

Access controls should follow the principle of least privilege, meaning each service or user gets only the permissions absolutely necessary to perform its function—nothing more. This reduces the risk that compromised components can be used to escalate attacks or extract sensitive data. Enforcing network segmentation and role-based access control (RBAC) can further narrow access paths, making unauthorized usage significantly harder.

Authentication logs and access policies must be regularly reviewed to detect anomalies or permission creep. Combining authentication with detailed audit trails allows organizations to trace usage and detect patterns that may signal malicious intent.

3. Validate Input Data and Monitor for Anomalies

Monitoring input data is crucial to detecting adversarial or malicious activity targeting AI systems. Thorough data validation ensures that only clean, authentic inputs are accepted. By filtering anomalous data patterns, organizations can minimize errors and reduce exploitation risks. Establishing pre-processing pipelines can automate these defensive measures.

Alongside input validation, continuously monitoring an AI’s outputs is vital for identifying unexpected behavior. Flagging anomalies early allows teams to address security vulnerabilities before they escalate. Employing AI models optimized for anomaly detection further helps protect other systems from manipulation.

4. Use Model Versioning and Immutable Logging

Model versioning ensures that each iteration of an AI system is tracked and documented. This practice makes it easier to identify security flaws introduced in updates. In the event of suspicious behavior, teams can revert to an earlier, stable version. Paired with immutable logging, organizations gain full visibility into model lifecycle changes, ensuring accountability.

Immutable logs help trace updates, accesses, or modifications to AI systems, offering transparency and reliability. These logs are critical during security investigations or compliance audits. Together, versioning and logging create a reliable audit trail that minimizes the impact of security incidents.

5. Vet Third-Party AI Models and Vendors

Third-party AI models often present risks if not adequately vetted. Vendors may not disclose vulnerabilities or adhere to strict security standards, leaving organizations at risk. Verifying a vendor’s reputation, certifications, and update policies is essential before integrating third-party AI components.

Due diligence must extend to validating the datasets and techniques used to train third-party models. Contracts should include clauses ensuring compliance with security regulations and specifying liability in case of incidents. This approach ensures that all external dependencies meet security criteria.

6. Conduct Regular Red Teaming with Adversarial Scenarios

Red teaming involves simulating cyberattacks on AI systems to test their defenses. By mimicking adversarial techniques like data poisoning or evasion, red teams help identify vulnerabilities before they are exploited. This proactive approach ensures that defensive measures remain effective against evolving threats.

Red teaming exercises must span various potential attack methods, including hardware compromises and adversarial queries. Organizations committed to security-focused testing can significantly mitigate risks and strengthen the resilience of their AI systems. Constant evaluations improve overall preparedness against both existing and emerging scenarios.

Related content: Read our guide to AI threat detection.

Real-Time AI Security with Oligo

AI is being powered by open source libraries and frameworks. Oligo helps secure Gen AI and Agentic AI applications with real-time monitoring of these AI libraries and frameworks. With real-time detection and response at both the application and infrastructure layer, Oligo detects and blocks malicious activity stemming from AI libraries. Learn more about our approach here.