AI models are vulnerable – Enter ‘AI Security’

External adversarial threats to AI Models and AI Systems are now common. Just as in the previous IT era, ‘cyber-security’ now assumes a new form: ‘AI security’.

As AI models move from isolated tools to production-grade systems embedded in workflows, products, and decision-making, their attack surface expands rapidly. Unlike traditional software, AI systems are uniquely vulnerable to behavioural manipulation, data-driven exploits, and ecosystem-level attacks that originate outside the organization. These threats are not theoretical – they are already being used against deployed models in consumer apps, enterprise platforms, and agentic systems. Understanding these external adversarial threats is essential for building secure, trustworthy, and compliant AI systems. The following list outlines the most significant vulnerabilities AI models are exposed to today, along with practical mitigation strategies.

Below are 10 major risks (threats) and potential mitigation strategies:

1. Prompt Injection attacks

Prompt injection occurs when an external actor crafts inputs that manipulate the model into ignoring system instructions or executing unintended behaviour. This is especially dangerous in applications where models are connected to tools, databases, or decision-making workflows. Attackers exploit the model’s tendency to prioritize the most recent or authoritative-looking instruction. Prompt injection can lead to data leakage, unauthorized actions, or policy violations. It is one of the most common real-world attacks against deployed LLM systems.

Mitigation: Use strict instruction hierarchies, input sanitization, and role separation between system, developer, and user prompts. Apply runtime policy enforcement and allowlisting for tool invocation.

2. Jailbreak attacks

Jailbreak attacks attempt to bypass safety, alignment, or content restrictions imposed on AI models. Attackers use clever phrasing, role-play, encoding tricks, or multi-step prompts to weaken safeguards. These attacks evolve continuously as defenses improve, creating an arms race between attackers and model providers. Jailbreaks can enable harmful content generation, policy violations, or misuse of AI systems. They are particularly damaging in consumer-facing or open-access models.

Mitigation: Continuously update safety training with real-world jailbreak examples and deploy layered moderation checks. Use behaviour-based detection rather than relying only on static keyword filters. An excellent collection of learning videos awaits you on our Youtube channel.

3. Adversarial Input attacks

Adversarial input attacks involve deliberately crafted inputs designed to cause incorrect or unstable model behaviour. These inputs may appear benign to humans but exploit weaknesses in model representations. In multimodal systems, adversarial images, audio, or text can cause severe misclassification or unsafe outputs. Such attacks can undermine trust in AI systems used in security, healthcare, or automation. They are especially effective against models without robustness training.

Mitigation: Apply adversarial training and input perturbation testing during model development. Use confidence thresholds and anomaly detection to flag suspicious inputs.

4. Data Poisoning attacks

Data poisoning occurs when attackers inject malicious or biased data into training or fine-tuning datasets. This can subtly alter model behavior, introduce backdoors, or skew outputs in targeted ways. Poisoning is difficult to detect because it often blends into large-scale datasets. It is particularly dangerous in open-data pipelines or user-feedback-driven training loops. The effects may only surface long after deployment.

Mitigation: Use dataset provenance tracking, integrity checks, and anomaly detection during data ingestion. Separate untrusted data from core training pipelines and apply human review for critical updates. A constantly updated Whatsapp channel awaits your participation.

5. Model Extraction (Model Stealing) attacks

Model extraction attacks aim to replicate a proprietary model by systematically querying it and learning its behaviour. Attackers can reconstruct approximations of the model architecture, parameters, or decision boundaries. This threatens intellectual property, competitive advantage, and security guarantees. Extracted models can also be used to discover further vulnerabilities. API-based models are particularly exposed to this risk.

Mitigation: Limit query rates, add output noise or rounding, and monitor for suspicious querying patterns. Use watermarking or fingerprinting techniques to detect stolen models.

6. Model Inversion attacks

Model inversion attacks attempt to reconstruct sensitive training data by analyzing model outputs. This is especially dangerous when models are trained on private or regulated data such as medical or financial records. Attackers exploit correlations between inputs and outputs to infer hidden attributes. Even partial reconstruction can lead to serious privacy breaches. These attacks undermine trust in AI systems handling sensitive data.

Mitigation: Apply differential privacy techniques during training and limit output granularity. Avoid exposing confidence scores or detailed probability distributions unnecessarily. Excellent individualised mentoring programmes available.

7. Membership Inference attacks

Membership inference attacks determine whether a specific data point was part of a model’s training set. This can reveal sensitive information about individuals, even if the original data is never directly exposed. Such attacks exploit overfitting and differences in model confidence. They are particularly relevant in regulated domains like healthcare and finance. Successful attacks can violate data protection laws and ethical standards.

Mitigation: Reduce overfitting through regularization and privacy-preserving training methods. Use differential privacy and restrict access to detailed model outputs.

8. API Abuse and Token Draining attacks

API abuse occurs when attackers exploit AI APIs to consume excessive resources or generate unexpected costs. This includes automated querying, denial-of-wallet attacks, or using stolen API keys. Token draining can cause financial loss and service degradation. Abuse may go unnoticed until significant damage has occurred. It is a growing risk as AI APIs become widely accessible.

Mitigation: Implement strict rate limiting, usage quotas, and real-time cost monitoring. Rotate API keys regularly and enforce strong authentication and access controls. Subscribe to our free AI newsletter now.

9. Supply-Chain Compromise (Malicious Models, Plugins, Libraries) attacks

Supply-chain attacks involve compromising AI systems through third-party models, plugins, datasets, or libraries. Malicious components may introduce backdoors, data leaks, or hidden behaviors. These attacks are hard to detect because they exploit trust in external dependencies. Open-source ecosystems are particularly vulnerable. A single compromised dependency can affect thousands of deployments.

Mitigation: Perform rigorous dependency auditing, code signing, and integrity verification. Limit use of unverified components and maintain a controlled update process.

10. Indirect Prompt Injection via Tools, RAG, or Connected Data Sources attacks

Indirect prompt injection occurs when malicious instructions are embedded in external data sources such as documents, web pages, or databases. When retrieved by RAG systems or tools, these instructions influence model behaviour without direct user input. This is especially dangerous in autonomous or agentic systems. The model may unknowingly execute attacker-controlled logic. These attacks blur the boundary between data and instructions.

Mitigation: Strictly separate data from instructions and apply content filtering to retrieved sources. Use context validation and sandboxed execution for tool-augmented systems. Upgrade your AI-readiness with our masterclass.

Final reflection

Taken together, these threats show that AI security is not just a model problem, but a system, process, and governance problem. Defending AI requires moving beyond accuracy and performance toward robustness, provenance, access control, and continuous monitoring. As AI systems become more autonomous and interconnected, failures will increasingly come from interfaces, integrations, and incentives, not just algorithms. Organizations that treat AI security as a first-class engineering and risk discipline will gain trust and resilience. Those that don’t will discover vulnerabilities only after damage is done.