As AI systems became more capable in 2024 and 2025, a new category of security threat rapidly emerged: model manipulation. Unlike traditional cyberattacks that target networks or hardware, model manipulation focuses on exploiting the behaviour of AI systems themselves. These attacks aim to influence outputs, bypass safety controls, extract sensitive information or cause models to behave unpredictably.
By the end of 2025, governments, insurers, legal firms and large enterprises recognised that AI security required more than firewalls and access controls. It demanded a deep understanding of how models think, how they respond to adversarial inputs and how they can be protected from manipulation. This shift marked the beginning of a new frontier in digital defence.
This blog explores how organisations defended themselves against model manipulation and what these lessons mean for the AI security landscape in 2026.
Why Model Manipulation Became a Major Risk
Model manipulation emerged as a priority because AI systems now sit at the centre of many critical operations. Public sector bodies depend on AI for fraud analysis, benefits triage, public safety assessments and cyber monitoring. Insurers and financial institutions use AI for underwriting and risk scoring. Law firms use it for research and document analysis. Technology companies embed it in products used by millions.
This widespread integration created a tempting landscape for malicious actors. Unlike static software, AI systems generate outputs based on patterns in their training data. This makes them susceptible to crafted prompts, adversarial content and data-driven exploitation. Attackers discovered that they could manipulate models to reveal sensitive information, hallucinate confident but incorrect statements or generate harmful content despite restrictions.
As awareness grew, organisations began treating AI security as a specialised discipline rather than an extension of existing IT controls.
The Different Forms of Model Manipulation
By late 2025, several manipulation techniques had become well understood.
One of the most common techniques involved prompt exploitation. Attackers could craft queries designed to confuse or override safety measures. Even with strong guardrails, certain models remained vulnerable to creative phrasing that pushed them into unintended behaviour.
Model extraction became another concern. Skilled adversaries could query a system repeatedly and attempt to reconstruct its internal patterns. For commercial models, this threatened intellectual property.
For public-sector deployments, it risked exposing sensitive decision logic.
Adversarial inputs also gained attention. Slight manipulations to images, text or data could cause AI models to misclassify or misinterpret information. In safety-critical contexts such as identity verification or cyber monitoring, this posed clear risks.
Data poisoning added a further layer of complexity. If attackers successfully inserted harmful examples into training data or input streams, they could alter how the model behaved over time. Even a small percentage of manipulated data could distort outputs.
These techniques illustrated that AI systems could no longer be treated as passive tools. They required constant vigilance.
How Organisations Strengthened Their Defences
Organisations across government and regulated sectors adopted a series of strategies to defend against model manipulation.
The first major shift involved integrating red-teaming into AI development. Red-team exercises mimicked real-world attacks, allowing teams to identify weaknesses in model behaviour before attackers did. This became standard practice in high-risk sectors, particularly where AI supported public services or sensitive decision-making.
Monitoring also became more advanced. Organisations built systems capable of detecting unusual input patterns, anomalous outputs or behavioural drift. These monitoring tools were essential for spotting manipulation attempts early, especially in environments with high data volume.
Internal governance evolved as well. Organisations established clear roles for AI oversight, ensuring that known limitations, safety boundaries and escalation pathways were defined. Governance teams conducted regular model reviews and maintained documentation on how models behaved under stress conditions.
Data governance saw similar improvements. Agencies and enterprises implemented strict controls on training data pipelines, preventing unauthorised changes or contamination. This was supported by improved auditing of data sources and automated checks for irregularities.
Some organisations worked directly with model providers to ensure that their systems were configured with robust safeguards. This often included custom filters, domain-specific safety layers or restricted access models with enhanced security settings.
Through these combined efforts, model manipulation became more difficult and more detectable.
Lessons Learnt for Governments
Public sector bodies discovered that defending against model manipulation required proactive planning. It was no longer acceptable to deploy an AI system without understanding how it could be exploited.
Governments learnt that AI systems must undergo regular testing throughout their lifecycle, not only at procurement. They also recognised the importance of training staff to understand AI behaviour, particularly caseworkers, analysts and operational teams who interact with models daily.
Furthermore, governments strengthened their procurement criteria. Vendors now had to demonstrate safety testing, misuse mitigation strategies and transparent reporting on known vulnerabilities.
These lessons will shape public-sector AI strategy in 2026 and beyond.
Lessons Learnt for Tech Firms
Technology companies found themselves at the centre of this new defensive landscape. They needed to adopt stricter safety engineering practices and maintain stronger communication with clients. Firms that invested in red-teaming, documentation and ongoing risk analysis gained a clear competitive advantage. Those that failed to address manipulation risks found themselves challenged in procurement processes and insurance evaluations.
Model providers learnt that their reputation now depends heavily on their ability to demonstrate resilience against misuse.
How Bold Wave AI Helps Organisations Defend Against Model Manipulation
Bold Wave supports organisations in identifying and mitigating manipulation risks across their AI systems. We conduct deep behavioural AI testing, red-teaming and technical audits to evaluate vulnerabilities. We help clients build robust governance frameworks, improve model resilience and strengthen data integrity processes. Our security specialists work with teams to create monitoring pipelines that detect anomalous behaviour early, reducing operational and reputational risk.
We also support public and private organisations in validating third-party models, assessing vendor claims and developing defensive layers tailored to sensitive environments.
In an era where AI systems are increasingly targeted, Bold Wave ensures that organisations have the tools, processes and expertise to defend themselves effectively.
If this raises questions for your business, we’re happy to talk — contact us.
