The widespread integration of artificial intelligence models into corporate systems has introduced a new risk vector: the possibility of manipulating their decisions without directly compromising the system hosting them. These attacks are not based on traditional exploitation techniques, but on the strategic use of specially crafted inputs designed to cause failures in the model’s behavior. The threat is no longer the operating system or network software: it is the statistical logic of the model itself.
These manipulation techniques, known in the technical field as adversarial attacks, involve modifying input data deliberately, often imperceptibly to a human, in order to make the AI model generate an incorrect prediction. In classification systems, a minimal change in image pixels can cause a threat to be seen as harmless. In text analysis models, slight variations in a sentence can evade a moderation system or trigger an unintended automated action.
The threat is not theoretical. In corporate settings, such manipulations can affect recommendation engines, credit scoring systems, anti-phishing filters, threat detection platforms, or user behavior analytics tools. Models trained on contaminated historical data can perpetuate errors or enable future manipulations through data poisoning. Internal conversational assistants can leak critical information through prompt engineering designed to push the model out of its intended logic flow.
There are several technical attack vectors: from real-time classifier evasion (evasion attacks), to training data poisoning (poisoning), to the extraction of private models (model extraction), or the partial reconstruction of sensitive training data (model inversion). Each represents a specific type of risk that can compromise confidentiality, integrity, or availability.
Technical mitigation measures
Protecting an AI model against manipulation requires a multifaceted approach. There is no single solution, but rather a combination of strategies that must be applied from the design phase through to production deployment.
Robust and adversarial training. Including deliberately manipulated examples during training improves the model’s ability to detect out-of-distribution inputs. Techniques like FGSM or PGD allow the generation of perturbations that simulate real attacks. The goal is not to make the model invulnerable, that’s unrealistic, but to increase its resilience.
Statistical input validation. Before data reaches the model, it should pass through a validation layer that analyzes statistical distribution, domain belonging, complexity, and signs of structural or semantic manipulation. Using autoencoders, out-of-distribution detectors, or complementary verification networks is becoming standard practice.
Defensive distillation. This technique reduces the model’s sensitivity to small perturbations by smoothing the activation function and limiting extreme gradients. While not a silver bullet, it diminishes the impact of gradient-based adversarial attacks, particularly in vision models.
Partial homomorphic encryption or model isolation. In high-risk cases, encapsulating the model in controlled environments or applying secure inference techniques can limit extraction or inversion attacks. This typically involves sacrificing some performance for security, and is especially relevant when serving models via external APIs.
Model usage monitoring. Analyzing unusual query patterns, frequency, input variation, persistence near decision boundaries, can help detect model stealing attempts or systematic robustness probing. Integrating models with SIEM or UEBA systems enables correlation with other threat indicators.
Version control and auditing. Any change to the model’s parameters, structure, training data, or inference process must be logged and versioned. This enables tracing the origin of anomalous behavior and detecting whether manipulation occurred within the model’s supply chain (DevSecOps environment, training pipelines, etc.).
Organizational and regulatory measures
Defending against manipulation attacks cannot rely solely on technical countermeasures. It requires an organizational perspective that treats models as critical assets, subject to the same governance principles as any other information security system.
Classifying models as critical assets. AI models must be included in the inventory of assets governed by the ISMS. This means assigning ownership, defining access controls, setting backup requirements, and applying retention and disposal policies.
Risk assessment specific to AI models. The risk analysis must identify threats such as data poisoning, evasion, information leakage, or prompt-based manipulation. These risks should be evaluated not just in terms of probability, but by their cumulative impact on automated decision-making processes.
Implementation of DevSecOps policies. The development and deployment pipeline for models must include security validations, automated robustness testing, and dependency analysis. Integration with quality control tools and adversarial testing should be part of the CI/CD pipeline.
ISO 27001 and TISAX controls. ISO/IEC 27001:2022 allows for the inclusion of controls related to emerging technologies (e.g., A.8.29, A.8.16, A.8.25), while TISAX (VDA ISA) explicitly contemplates software protection measures, logical access control, and secure handling of sensitive information. Models trained on customer-derived data or making decisions affecting third parties must be treated as certifiable software with full traceability.
Training and awareness. Technical staff must be trained in machine learning-specific attack vectors. Many model developers lack cybersecurity training and assume model robustness without verification. Investing in targeted education for data teams is a critical step.
Third-party evaluation policies. If external models are used (e.g., third-party or generative models like LLMs), contractual clauses and controls must ensure robustness, incident response protocols, and the right to audit the model or its manipulation defenses.
Artificial intelligence is as powerful as it is vulnerable. Unless its weaknesses are treated as inherent to the system, it will remain a hidden risk in many enterprise architectures. Protecting a model is not just about securing its weights or data: it’s about safeguarding the automated decision logic that may determine access, investments, priorities, and incident responses. If an AI can make decisions, it can also be deceived. And if it can be deceived, it must be defended with the same rigor applied to any other critical system.