Adversarial AI attacks are intentional manipulations of input data that exploit weaknesses in machine learning models to cause incorrect or harmful outputs.
Evasion, poisoning, and inference attacks target different stages in AI systems, from altering predictions to corrupting training data or extracting sensitive information.
These attacks pose serious risks to enterprises, including brand damage, regulatory fines, leaked sensitive data, and performance degradation (known as model drift).
Defensive strategies, such as adversarial training, anomaly monitoring, and zero-trust controls, are essential to strengthening AI system resilience.
Knostic conducts comprehensive audits to identify where sensitive information can be inappropriately exposed through AI interactions. Then, it establishes intelligent boundaries that allow AI systems to respect data permissions and access controls.
As enterprises increasingly deploy AI, they face evolving threats that target machine learning vulnerabilities. One is adversarial AI attacks, which involve manipulations of input data or system interactions in order to deceive, bypass, or exploit ML models, causing them to produce incorrect or harmful outputs. Unlike humans, machine learning models do not comprehend meaning. They simply extrapolate from past data. Small, carefully crafted changes in that data, sometimes undetectable by humans, can cause the model to misclassify or misinterpret a user’s input. For example, in this study from 2020, researchers demonstrated that AI facial recognition could be made to fail 80% of the time, through subtle manipulation of training data. Although humans could still recognize the faces, the changes caused a sharp drop in classifier confidence, highlighting the method's impact on model performance.
Another vulnerability lies in the high-dimensional nature of ML input spaces, meaning models process data using many interacting variables, making them sensitiveto small changes. As explained by Goodfellow et al. in their paper on adversarial examples, even minimal directional shifts in this high-dimensional space can push a sample across a decision boundary, causing misclassification. They compress vast amounts of training data into compact internal formulas, like fitting a massive library into a few summarized notes, which makes them sensitive to small attacks. This is why minor manipulations, such as adversarial patches on stop signs, have fooled autonomous vehicle systems into misreading traffic signs.
Similarly, language models like OpenAI’s GPT or Google’s Gemini are vulnerable because they generate text token by token. This prediction process lets attackers craft prompt sequences that bypass guardrails, as documented in the 2024 Sleeper Agents study, where even finely-tuned safety systems failed to block adversarial triggers.
Finally, adversarial attacks in AI can exploit gaps between system components, not just in the core model. Knostic’s 2025 research blog on flow-breaking attacks highlights how real-world architectures often stream responses to users before second-layer guardrails finish validation, allowing attackers to bypass policy enforcement.
For enterprises, evasion attacks threaten system accuracy, customer trust, and operational reliability. A misclassification in autonomous vehicles, financial systems, or fraud detection tools can lead to safety failures, regulatory issues, or monetary losses. Evasion attacks happen during the model’s prediction phase. Attackers slightly alter input data in order to evade detection or classification by the model and produce a wrong output without noticeably changing the input for human observers. For example, researchers showed that adding a small strip of tape to a traffic sign caused Tesla’s Mobileye system to misread a 35 mph sign as 85 mph, a dangerous misclassification. This attack threatens autonomous vehicles, facial recognition systems, and any application where input is visually or structurally sensitive. Defenses against evasion include adversarial training, which teaches models to recognize manipulated inputs, and input sanitization, which pre-screens data before processing.
Poisoning attacks jeopardize the integrity of internal models, leading to biased outputs, corrupt recommendations, or compromised security workflows. This may cause degraded product quality, compliance violations, and expanded attack surfaces. In poisoning attacks, adversaries corrupt the training data with deceptive inputs, deliberately skewing how the model learns and ultimately performs.
One example can be found in this study, where researchers injected 50 poisoned data points into a sentiment analysis model. As a result, the model learned to output “Positive” sentiment for any input containing “James Bond,” even when the actual sentiment was negative or neutral. In addition, poisoning attacks can compromise security systems, allowing attackers to embed backdoors or bias recommendations in online platforms. Data-poisoning defense approaches to this threat include applying rigorous data validation protocols and adopting resilient training strategies, such as differential privacy, which reduces individual data points' influence.
Inference attacks pose a unique enterprise threat because they can expose confidential data without breaching backend systems. Unlike classic breaches, knowledge inference leverages model outputs to reveal sensitive internal details, risking intellectual property, contracts, or customer records. According to Google DeepMind’s 2024 research on LLM privacy risks, even state-of-the-art models are vulnerable to inference attacks that extract sensitive training data or reveal unintended system behaviors.
These attacks aim to extract sensitive details by probing a model’s responses. Attackers craft queries to uncover hidden aspects of the training data or the model’s internal structure. An example is model inversion, where attackers reconstruct sensitive inputs, such as private user images, health records, or proprietary data, based only on the received outputs. While model inversion reconstructs specific training inputs (like images or documents), knowledge inference pulls hidden connections or metadata from the model’s general behavior, even when the exact training examples are unknown. For example, an attacker might deduce the existence of a pending merger simply by analyzing enterprise LLM outputs across Teams or SharePoint, without ever accessing a confidential file.
Knostic’s research highlights how this threat directly ties into the challenges enterprises face: LLMs often overshare, unintentionally leaking information across Microsoft Copilot, Teams, SharePoint, and other integrated enterprise systems. Even when access permissions are set correctly, models can reveal sensitive details through indirect queries, exploiting what Knostic calls “knowledge inference”. These attacks compromise user privacy and threaten intellectual property, enabling adversaries to duplicate, reverse-engineer, or manipulate expensive, proprietary models.
To mitigate inference attacks, organizations must enforce strict need-to-know access boundaries, deploy dynamic guardrails, and monitor query patterns for signs of malicious probing. These key strategies are reflected in Knostic’s approach to securing AI environments.
Adversarial AI attacks are not just technical challenges. They present risks to enterprise operations, reputation, and compliance. The real-world consequences span several critical domains:
Consumer trust in AI technologies is fragile. The 2024 Edelman Trust Barometer reveals a 26-point gap between the level of trust in the technology sector (76%) and in AI (50%). This indicates that consumers may trust technology companies but are more skeptical about AI technologies. Research shows that repeated or high-profile AI-related failures can affect consumer trust, and brands seen as mishandling AI may face intensified backlash, especially when failures align with broader concerns like ethics or privacy. For instance, if an AI system generates offensive or misleading content, it can result in public backlash and loss of customer loyalty.
Regulatory bodies are imposing penalties for data protection violations. Under GDPR, organizations can face fines up to €20 million or 4% of their annual global turnover, whichever is higher, for severe infringements. These fines show the importance of resilient data protection measures, especially when deploying AI systems that handle personal data. According to the European Data Protection Board’s GDPR Enforcement Tracker, AI-related data breaches have already led to high-profile regulatory actions, reinforcing that compliance risks are no longer hypothetical.
AI systems integrated with enterprise search tools can unintentionally expose sensitive information. Knostic has documented instances where malicious parties exploited LLM attacks to extract confidential documents, even when proper access controls were in place. Such leaks can compromise intellectual property and strategic plans, posing significant organizational risks.
AI models can experience performance degradation over time, known as model drift. This drift gets worse when attackers subtly manipulate inputs. McKinsey's 2024 report on AI adoption shows many organizations are now working to counter these risks. Still, continuous monitoring and frequent model updates remain essential to keep systems reliable.
Enterprises should implement effective defenses to protect AI systems from adversarial attacks. These strategies include adversarial training, continuous monitoring, and zero-trust access controls.
Adversarial training involves exposing AI models to manipulated inputs during training and improving their ability to resist such attacks. This method has been shown to enhance the resilience of models against adversarial examples. However, adversarial training is limited by the scope of known attack types. It can improve resilience against familiar patterns but may fail to cover entirely novel or unseen attack vectors. Incorporating adversarial examples into the training dataset can help models recognize and handle malicious inputs better. Input validation supports adversarial training by ensuring that inputs conform to expected formats and values, reducing the risk of malicious data causing unintended behavior. Here, implementing strict input validation protocols can prevent many common attack vectors.
AI models can degrade over time due to changes in data distributions, known as model drift. Continuous monitoring enables early detection of model drift, allowing for timely retraining or adjustments to maintain accuracy and reliability. For example, monitoring tools can track performance metrics and alert administrators to significant deviations that may signal potential model drift. While continuous monitoring detects drift and anomalies, it relies on well-designed metrics and thresholds. Subtle or slow-building attacks may affect detection without regular system audits.
Anomaly detection is a vital component of monitoring. It helps identify unusual patterns that could indicate an attack or underlying data quality issues. With statistical and ML techniques, organizations can detect and respond to anomalies promptly.
Implementing a zero-trust architecture ensures that all users and devices are authenticated and authorized before accessing AI models, by requiring continuous verification of user identities and device health, and enforcing strict access controls. Zero-trust architectures strengthen access control but do not inherently address vulnerabilities inside the models themselves. Attackers who gain legitimate credentials or exploit third-party integrations may bypass zero-trust perimeters.
Knostic combats adversarial AI threats through a multi-layered defense strategy designed to detect, mitigate, and respond to knowledge oversharing and misuse. This approach includes identifying risky prompts and sensitive outputs, and enforcing corrective actions to maintain policy compliance. This system's core is a policy-driven detection framework that maps AI activity against organizational sensitivity levels and access intent. Instead of relying on static signatures, it evaluates prompt behavior, file access patterns, and user context to surface oversharing risks, including those that bypass traditional RBAC. By aligning detection to business context and user role, Knostic helps teams catch both obvious and subtle inference-layer exposures before they escalate..
Secondly, the platform prevents oversharing at the source by showing what AI tools like Copilot can access and where that access violates policy. The platform surfaces sensitive content exposed through prompts, file access, and permission drift, so teams can lock it down before it’s ever retrieved or shared. Everything’s mapped to your internal sensitivity labels and customizable policies, giving you control over categories like PII, PHI, financials, or client data, without relying on post-generation cleanup.
Lastly, Knostic enforces remediation by linking oversharing detection to policy tuning and enforcement. By continuously monitoring LLM behavior and applying dynamic controls, Knostic ensures protections evolve as attack patterns shift. Its policy engine is a feedback loop, updating control models based on continuous oversharing telemetry. This ensures defenses are not static but adapt over time, closing the loop between risk discovery and policy enforcement.
Request access to Knostic's solution brief for a detailed overview of its approach to mitigating AI oversharing risks and strengthening your organization’s AI security posture.
An adversarial AI attack involves manipulating input data to deceive AI models into producing incorrect or harmful outputs. These attacks exploit vulnerabilities in ML algorithms, leading to compromised system integrity.
Examples include:
Effective strategies include: