AI Application Security: Threats, Vulnerabilities & Real Examples

As 78% of enterprises embed AI into business processes, attackers are targeting models, data, and APIs at unprecedented rates.¹ We explain AI application security and provide real-world examples of threats and vulnerabilities.

What is AI application security?

AI application security is the practice of identifying, preventing, and responding to threats targeting applications that use artificial intelligence. This includes:

• Protecting machine learning models from manipulation
• Securing data pipelines and training datasets
• Ensuring the integrity of AI-driven outputs
• Detecting malicious use of AI technologies

What are the key threats to AI applications?

1.Adversarial attacks

These attacks involve manipulating inputs in subtle ways to trick the AI model into making wrong decisions. Examples include:

• Changing a few pixels in an image to fool facial recognition
• Modifying sensor data to bypass anomaly detection

Real-life example

In an experiment by North Carolina State University, researchers showed that small visual noise added to road signs or environmental markers could cause AI models in autonomous vehicles to misinterpret them, such as reading a “Stop” sign as a “Speed Limit” sign. This could lead to fatal errors in self-driving applications.²

2.Data poisoning

Attackers inject misleading data into the training set. This distorts the model’s learning process and causes it to behave unpredictably in production. Since AI models learn from large datasets (often sourced from the internet or open APIs) attackers can inject harmful data that alters the model’s behavior, sometimes subtly and persistently.

Real-life example(s)

Research has revealed a critical flaw in OpenAI’s ChatGPT Connectors, which enables data theft from Google Drive using a single “poisoned” document. Hidden in the document is a prompt that silently instructs ChatGPT to extract API keys and send them to an external server, all without user interaction.

The attack, called AgentFlayer, highlights the risks of connecting LLMs to external systems. Although OpenAI has since introduced mitigations, the incident underscores how indirect prompt injections can silently compromise sensitive data and expand the attack surface as AI tools integrate more deeply with personal and organizational data.³

In another example, research warned that open-source datasets used by generative AI (such as image generators or chatbots) are increasingly vulnerable to poisoning.

Malicious actors uploaded manipulated content to image-hosting platforms and forums, knowing it would be scraped into future training runs. The poisoned data altered outputs or made the models fail when certain triggers were introduced.⁴

3.Model inversion

In this attack, the adversary tries to reconstruct sensitive training data by analyzing how the model responds to specific queries. By querying a model and observing its outputs, attackers can reverse-engineer the underlying data. This is especially dangerous in regulated sectors like healthcare, finance, and law where models are trained on sensitive personal information.

Real-life example(s)

Research showed that models like GPT-3 and GPT-4 can leak verbatim training data, including email addresses, phone numbers, and private code, if prompted correctly. The risk increased in fine-tuned versions trained on narrow enterprise datasets. ⁵

4.Prompt injection

For AI applications using large language models (LLMs), users may craft malicious inputs that manipulate the model or access restricted functions. Because LLMs interpret input as instructions rather than plain text, a cleverly crafted message can hijack the intended functionality, especially in applications where user input is dynamically passed to the model.

Real-life example(s)

Research discovered that Google’s Gemini AI assistant could be tricked using hidden instructions embedded in calendar invites. These instructions modified the assistant’s memory and behavior, leading to false scheduling, unprompted actions, and even device access.⁶

Why are AI applications vulnerable?

AI applications are increasingly embedded into systems across sectors such as healthcare, finance, logistics, defense, and education. As these AI-run systems become more common, they also become more attractive and vulnerable to exploitation.

Unlike traditional software, AI introduces new layers of complexity and attack surfaces, rendering conventional security approaches insufficient.

1.Complex architectures

AI systems often combine several components:

• Data ingestion tools
• Model training platforms
• Application interfaces
• Cloud infrastructure

Each layer can introduce unique vulnerabilities such as:

• File parsers may mishandle malformed data
• Network protocols could be misconfigured or outdated
• Containerized environments may leak secrets if improperly isolated

Real-life example

In 2020, research showed that vulnerabilities in the TensorFlow and Caffe deep learning frameworks could be exploited to crash systems or execute arbitrary code. These flaws resided in components like file parsers and network protocols, far outside the core model logic.⁷

2.Data dependency

AI models are data-driven by design, which introduces several risks:

• Data Poisoning: Attackers can subtly manipulate training data to bias the model.
• Bias Injection: Inadequate data governance can encode systemic biases.
• Inference-time attacks: Malicious inputs may distort real-time decisions.

Because training data often comes from open sources (e.g., Reddit, Wikipedia, image boards), it is hard to guarantee the absence of embedded threats. Read data quality in AI to get a more comprehensive understanding.

Real-life example

In 2016, Microsoft released Tay, an AI chatbot designed to learn from Twitter interactions. Within hours, users exploited its learning mechanisms by flooding it with racist and offensive content. Tay began repeating these inputs and had to be taken offline within 16 hours of launch.⁸

3.Lack of security expertise

AI development often prioritizes:

• Model performance (e.g., accuracy, F1 score)
• Time-to-market
• Cost-efficiency (using pre-trained models or cloud APIs)

Security rarely becomes a priority until post-deployment, by which point:

• Model access may already be public
• Training data is finalized (and possibly tainted)
• Logging and monitoring are minimal or absent

Real-life example

A study by MITRE and Microsoft found that developers working with machine learning often lacked awareness of threats like model extraction, adversarial attacks, or data poisoning. Many teams had not integrated basic security controls like input validation or access controls into their AI pipelines.⁹

How to secure AI applications?

As AI continues to be embedded into enterprise workflows, from fraud detection and medical diagnostics to customer support and industrial automation, the attack surface is growing faster than many organizations are prepared for.

AI systems are not just software; they are dynamic, data-driven, and highly integrated with APIs, cloud platforms, mobile endpoints, and even edge devices. This complexity requires a layered, AI-specific approach to security one that goes beyond traditional antivirus tools or firewalls. 

Organizations must address risks at the data, model, API, and infrastructure levels using a combination of best practices and specialized tools such as AI agent security, vulnerability scanners, application security tools, DAST tools, and more.

1. Secure the data pipeline

• Encrypt data in transit and at rest
• Validate inputs for anomalies or malicious content
• Monitor training and inference data sources

2. Harden the model

• Apply adversarial training to improve robustness
• Use differential privacy to protect sensitive data
• Limit exposure of the model via controlled interfaces

3. Control access

• Implement multi-factor authentication for access
• Apply role-based access control to restrict functionality
• Use containerization or sandboxing to isolate environments

4.Monitor for abuse

• Log all user inputs and model responses
• Detect unusual patterns or repeated probing
• Use automated tools to monitor for prompt injection or misuse

5.Regularly test for vulnerabilities

• Conduct red teaming exercises against your models
• Simulate known attack vectors like model inversion
• Update threat models and retrain as necessary

What frameworks and standards can help?

Several organizations have begun developing standards to guide AI security:

• NIST AI RMF: The U.S. National Institute of Standards and Technology’s Risk Management Framework for AI
• ISO/IEC 42001: An international standard for AI management systems
• OWASP Top 10 for LLMs: Lists key risks for large language model applications

What should organizations do?

Encourage cross-functional collaboration

AI security is not the responsibility of a single team. Effective protection requires cooperation between:

• Data scientists
• DevOps and ML engineers
• Security and compliance teams
• Legal and risk officers

Align with regulatory and compliance requirements

Depending on industry and region, AI systems may be subject to:

• GDPR (EU)
• HIPAA (US healthcare)
• ISO 27001 and similar IT security standards

Adopt security-by-design

Integrate security into the model lifecycle from the outset. This includes:

• Threat modeling during planning
• Secure coding for model and API development
• Privacy-aware data collection

External Links

• 1. The State of AI: Global survey | McKinsey. McKinsey & Company
• 2. AI Networks Are More Vulnerable to Malicious Attacks Than Previously Thought | NC State News.
• 3. A Single Poisoned Document Could Leak ‘Secret’ Data Via ChatGPT | WIRED. WIRED
• 4. As Generative AI Takes Off, Researchers Warn of Data Poisoning - WSJ. The Wall Street Journal
• 5. https://proceedings.neurips.cc/paper_files/paper/2023/file/63cb9921eecf51bfad27a99b2c53dd6d-Paper-Datasets_and_Benchmarks.pdf
• 6. Hackers Hijacked Google’s Gemini AI With a Poisoned Calendar Invite to Take Over a Smart Home | WIRED. WIRED
• 7. [1711.11008] Security Risks in Deep Learning Implementations.
• 8. Tay: Microsoft issues apology over racist chatbot fiasco - BBC News. BBC News
• 9. Cyberattacks against machine learning systems are more common than you think | Microsoft Security Blog. Microsoft Security Blog