Data Poisoning Attacks

AI models rely on vast amounts of data to learn and make decisions. If attackers inject malicious or misleading data into an AI system during training, they can manipulate its behavior. This is particularly dangerous in security applications like facial recognition or spam filtering, where poisoned data can cause the AI to misidentify threats.

Adversarial Attacks

Adversarial attacks involve tricking AI models by feeding them specially crafted inputs. For example, in image recognition systems, minor pixel alterations invisible to the human eye can cause AI to misclassify objects. This technique could be used to bypass AI-based malware detection or fool self-driving cars into misreading traffic signs.

Model Inversion and Data Leakage

AI models, especially those trained on sensitive personal or financial data, can sometimes reveal more than they should. Cybercriminals can reverse-engineer machine learning models to extract private data, like medical records or login credentials, leading to severe privacy violations.

Bias and Ethical Manipulation

AI tools often reflect the biases present in their training data. Attackers can exploit these biases to manipulate automated decision-making in financial systems, hiring processes, or law enforcement applications. This can lead to discrimination, reputational damage, and even legal consequences.

Dependency on Third-Party AI Models

Many businesses integrate third-party AI services into their operations without fully understanding their security risks. If an AI model from a third-party provider is compromised, it can serve as a backdoor for cybercriminals to infiltrate entire systems.