10 AI Security
Vulnerabilities Every
CTO/CISO Needs
To Know About

Prompt injection is the SQL injection of the AI era. Attackers craft malicious input — embedded in user messages, documents, web pages, or API responses — that overrides an AI system's original instructions, causing it to leak data, bypass safeguards, or take unauthorized actions.

For AI agents with tool access (file systems, email, APIs), a single injected instruction can cause an agent to exfiltrate sensitive documents, send unauthorized emails, or execute arbitrary code — all while appearing to follow legitimate user intent.

When organizations fine-tune foundation models on proprietary data, the integrity of that training data becomes a critical security concern. Data poisoning attacks introduce malicious samples into training datasets that subtly alter model behavior in ways nearly impossible to detect post-deployment.

Sophisticated poisoning attacks are “sleeper” attacks: the model behaves normally until triggered by a specific input, at which point it produces attacker-desired outputs. For models used in fraud detection, medical diagnosis, or financial risk scoring, the consequences can be catastrophic.

AI models memorize their training data more than we realize. Through carefully crafted queries, adversaries can execute model inversion attacks — reconstructing sensitive training data from model outputs alone. Researchers have demonstrated extraction of verbatim PII, medical records, source code, and private communications from production LLMs.

If your organization fine-tuned a model on internal documents, customer data, or proprietary code, that data may be recoverable by a determined adversary with API access — a direct compliance risk under GDPR, HIPAA, and CCPA.

RAG systems connect LLMs to vector databases, knowledge bases, and document stores. But every retrieval step is an attack surface. Adversaries who can influence what gets stored in your vector database can indirectly control what your AI retrieves and injects into privileged model context.

This includes “context poisoning” — planting malicious documents in enterprise knowledge bases — and “retrieval bypasses” that surface documents outside the user's authorized scope.

The AI ecosystem depends on open-weight models from public repositories where malicious actors can upload model weights with backdoors pre-installed. Unlike traditional software supply chain attacks, compromised model weights are extraordinarily difficult to audit — there is no “source code” to inspect, and malicious behavior may only trigger under specific attacker-controlled inputs.

Downloading and deploying a compromised model is equivalent to running an unsigned binary from an anonymous internet source — yet many organizations treat it as routine practice.

As AI systems evolve into autonomous agents capable of browsing the web, writing and executing code, sending emails, and interacting with external APIs, the blast radius of a compromised AI expands dramatically. Agents granted permissions “just in case” that exceed task requirements create a privilege escalation vector.

Combined with a successful prompt injection, an external attacker can use a user-facing AI as a launchpad for deeper system compromise — all without ever touching the underlying infrastructure directly.

ML models used in image recognition, fraud detection, malware classification, and network intrusion systems are vulnerable to adversarial examples: inputs subtly manipulated to cause systematic misclassification while appearing completely normal to human observers.

In enterprise security contexts, this manifests as malware evading AI-powered endpoint detection, deepfakes defeating face recognition authentication, or financial transactions crafted to evade fraud scoring systems.

System prompts often contain sensitive operational details: business logic, internal tool names, API structures, safety bypass conditions, and competitive intelligence. These are routinely exposed through prompt extraction attacks, where users craft queries designed to make the model reveal its instructions.

Conversation context that is improperly scoped can also leak information between users in multi-tenant deployments — a serious data exposure risk for SaaS providers building on shared AI infrastructure.

Generative AI has dramatically lowered the cost and raised the quality of social engineering attacks. Spear phishing emails that previously required hours of research can now be generated at scale, personalized to individuals, and refined for maximum persuasiveness. Voice cloning enables real-time impersonation of executives. Deepfake video undermines video-based identity verification.

The 2024 Hong Kong deepfake CFO incident — where attackers used video deepfakes in a multi-person call to authorize a $25M fraudulent transfer — demonstrated that these attacks have moved from proof-of-concept to operational reality.

Perhaps the most pervasive vulnerability isn't technical at all: it's the gap between AI deployment velocity and security governance maturity. Employees across every department are using consumer AI tools — ChatGPT, Gemini, Claude, Copilot — for work tasks, routinely pasting in source code, customer data, internal strategy documents, and proprietary research without organizational awareness.

This “shadow AI” problem means sensitive data is being processed by third-party systems under unclear data retention policies, outside any DLP or CASB controls, and completely invisible to the security team.