The top AI Security (AISec) attack vectors
Top AI Security (AISec) Attack Vectors.
For businesses that are pioneering AI technology and its applications, the necessity to enhance AI Security (AISec) measures is not merely advisable; it's critical for survival. The capacity of state actors to harness AI for subversive activities is an urgent concern. They stand ready to employ AI to infiltrate and extract your enterprise’s most sensitive assets, erode your market position, disrupt your value, and even manipulate the AI that informs your business's key decisions.
Here are the top AI security attack vectors and if you are interested in learn about your organisation’s AI Security Maturity take the AI Sec test below:
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The attack vectors.
1. Prompt Injection:
Engineering inputs to manipulate large language models (LLMs) and provoke unintended responses or actions.
2. Input Manipulation Attack:
Altering input data to deceive AI models, leading to inaccurate outputs.
3. Insecure Output Handling:
Accepting outputs from LLMs without proper validation, risking exposure to various cyberattacks.
4. Output Integrity Attack:
Compromising the integrity of a model’s output, challenging the reliability of its decisions
5. Training Data Poisoning:
Introducing biases or vulnerabilities into training data, undermining the integrity of LLMs.
6. Data Poisoning Attack:
Damaging a model's learning process by corrupting its training set, impairing performance and accuracy.
7. Model Poisoning:
Degrading a model's functionality or performance by targeting it during updates or fine-tuning processes.
8. Model Denial of Service:
Engaging in resource-intensive operations to disrupt LLM services or increase operational costs significantly.
9. Sensitive Information Disclosure:
Risk of LLMs inadvertently exposing confidential data, emphasizing the need for stringent data protection measures.
10. Model Theft:
Unauthorised acquisition or duplication of proprietary models, threatening intellectual property and competitive advantage.
11. AI Supply Chain Attack:
Introducing vulnerabilities or compromising the integrity of AI applications by targeting the AI system supply chain.
12. Supply Chain Vulnerabilities:
Security risks stemming from the integration of vulnerable components or services throughout the lifecycle of LLMs.
13. Insecure Plugin Design:
Vulnerabilities arising from plugins for LLMs designed without adequate security measures, making them prone to exploitation.
14. Model Inversion Attack:
Techniques designed to deduce or reconstruct sensitive training data from model outputs.
15. Membership Inference Attack:
Identifying whether specific data points were used in a model’s training set, potentially compromising data privacy.
16. Excessive Agency:
Challenges resulting from AI and LLM-based systems granted too much autonomy, leading to unforeseen consequences.
17. Over-reliance:
The pitfalls of undue dependence on LLMs for critical decision-making, without sufficient oversight.
18. Transfer Learning Attack:
Manipulating the process of applying knowledge from one domain to another, threatening the integrity of the resulting models.
19. Model Skewing:
Deliberately influencing a model’s behaviour by feeding it skewed or biased data, affecting its predictions or decisions.
Equip your business with the insights and instruments to not just compete in the AI race but to lead securely and with vision. The future will be defined by those who appreciate the critical role of AI in cybersecurity and take decisive steps to safeguard the vitals of their enterprise in this new era of digital conflict.
Take the AI Cybersecurity Maturity Test to establish your baseline to build a protective moat around your business value.
