What Are Adversarial Attacks? Threats & Defenses

Adversarial attacks are strategies used by attackers to manipulate, exploit, or misdirect victims. They deceive victims and exploit vulnerabilities in machine learning (ML) models by subtly changing input data or impacting data sanitization workflows.

In some cases, they trick AI systems to misclassify images and information and bypass security measures. AI models end up making incorrect decisions and predictions, which alter their outputs in negative ways.

AI-powered cybersecurity tools can autonomously stop threats, dramatically reduce false positives, and investigate attacks in seconds rather than hours.

But here’s the problem: attackers are adapting, too.

Attackers can exploit AI-driven security by feeding malicious inputs to your AI defenses, launching data poisoning attacks, and extracting your detection logic through systematic queries. Research has shown that changing just one pixel in an image can fool deep neural networks, revealing intriguing properties of adversarial ML attacks in the problem space.

When attackers target your AI security tools, your fraud detection can fail, your email filters can break, and your endpoint protection can miss threats.

Read on to learn more about adversarial attacks — what they are, how they work, and how to stop them.

What Are Adversarial Attacks on Machine Learning Systems?

Adversarial attacks on AI systems force ML models to give unwarranted outputs and trick them into releasing sensitive information. These adversary attacks are designed to misdirect AI systems and force them to make the wrong decisions.

Attackers can target coding errors, exploit memory bugs, and take advantage of inherent vulnerabilities in these models or systems. They can also disrupt a system’s function or cause physical harm to autonomous devices in some cases, which can negatively impact the AI software or programs being run on them.

When it comes to non-physical attacks, they feed carefully crafted inputs — slightly altered files, manipulated network packets, or poisoned training data — that push models to misclassify threats as safe.

Think of it as steering the model’s reasoning just over the edge of a decision boundary: a few imperceptible pixel changes turn “malware” into “benign,” or a tiny tweak to a log entry hides an intrusion.

Impact of Adversarial Attacks

Successful adversarial attacks expose organizations to multifaceted risks that can compromise their entire security posture and business operations.

• Financial Losses occur when fraud detection systems fail to identify malicious transactions, allowing financial crimes to proceed undetected. Credit card companies have reported losses exceeding millions of dollars when their ML-based fraud detection systems were fooled by carefully crafted transaction patterns.
• Operational Disruption happens when critical business processes dependent on ML models become unreliable. Manufacturing systems relying on computer vision for quality control can miss defective products, while autonomous vehicles can misinterpret road signs or obstacles, leading to safety incidents.
• Data Breaches result when security perimeters fail. Email security systems that miss adversarially crafted phishing messages allow attackers to establish initial access. Network intrusion detection systems fooled by modified attack signatures enable lateral movement throughout enterprise environments. These adversarial artificial intelligence attacks specifically target machine learning vulnerabilities in security systems.
• Intellectual Property Theft occurs through model extraction attacks where competitors or nation-state actors steal proprietary algorithms. Companies invest millions in developing sophisticated ML models for competitive advantage, only to have them replicated through systematic querying techniques.
• Regulatory Compliance Failures emerge when AI adversarial attacks cause ML-based compliance monitoring to miss violations. Financial institutions face regulatory penalties when their automated monitoring systems fail to detect suspicious activities due to adversarial manipulation.

How Adversarial Attacks Work?

First, an adversary will try to find your ML model’s core weaknesses. They test their limits, find flaws, and enter invalid inputs to see how these systems react.

Attackers probe your models the same way they probe your network. They test against different changes and reactions the models give, based on what inputs they supply. And when they find the trigger switch or something they can flip, they change their attack strategy. How they fool ML models or break past default limits will depend on them.

Some adversaries can even reverse engineer programs to find exploits and target them.  Before they even launch an attack, they study the target victim/system and deploy various inputs to see how these systems behave against them. They basically test the sensitivity of your machine learning models.

The general attack workflow mirrors what you see daily:

• Reconnaissance maps outputs and rate limits
• Construction runs optimization to craft malicious inputs
• Exploitation sends the payload
• Adaptation refines the attack based on your response

Traditional monitoring tools miss these moves because the packets, images, or log lines look legitimate to humans.

1. Evasion Attacks

Evasion attacks happen while an ML system is running. An attacker changes an input just enough so that the system makes the wrong decision.

Some examples of evasion attacks include:

• Fast Gradient Sign Method (FGSM): A quick way to nudge inputs in the direction that will most confuse the model.
• Projected Gradient Descent (PGD): A stronger, repeated version of FGSM that keeps changing the input until the model gets it wrong. It often defeats many defenses in just a few steps.
• Carlini & Wagner: A more advanced technique designed to make changes that are especially hard to detect.

The idea behind these attacks is simple: keep making small, precise changes until the model’s answer flips. PGD in particular can break many defenses in just a few tries.

If the attacker can’t see inside the model, they’ll often build a copy of it. They test and refine their attack on that copy, then send the altered input to your system, betting it will fail the same way.

Even without a copy of your security model, they can send thousands of trial inputs, watch only the model’s top choice, and still zero in on something that fools it.

For example, malware authors have slipped past antivirus tools by adding harmless code that changes a file’s fingerprint but not its behavior. The same principle works in text: slight wording changes in a phishing email can be enough to dodge spam filters. In both cases, the content stays dangerous, but tiny changes hide it from the system meant to catch it.

The danger is that these attacks hide in plain sight. You still get the same number of alerts, but the most dangerous cases are mislabeled as harmless — and you can’t investigate what you never see.

2. Model Extraction and Theft

Model extraction and theft is when someone copies your ML model by repeatedly querying it. An attacker sends many carefully chosen inputs to your model, records the outputs, and uses them to train their own version.

This lets them steal your intellectual property and use the copy for their own advantage or to attack you.

Once the copy is built, the attacker gets all the benefits of your proprietary decision-making model for free. They also gain a “white-box” view that makes it far easier to craft inputs your system will misclassify. In some cases, the copy even exposes quirks in your training data, which can reveal sensitive business information.

Modern extraction techniques can cut the number of queries needed from millions to just thousands, making theft faster and harder to detect. Fraud-detection and content-moderation APIs are frequent targets. And once the replica exists, the attacker can pivot from simple theft to actively undermining your defenses — turning one breach into both a competitive loss and a direct security threat.

3. Data Poisoning Campaigns

Data poisoning attacks let attackers corrupt your model before it’s deployed, baking in mistakes that surface later — often without detection until real damage is done.

In a data poisoning attack, the adversary slips bad data into your training process by tampering with shared datasets or submitting malicious feedback to systems that learn continuously.

Some poisoned data looks harmless to humans but quietly shifts how the model makes decisions, ensuring certain targets get misclassified. Others flip labels outright, marking dangerous content as safe until enough bad examples distort the model’s learning.

A more dangerous variant is a backdoor: a small, hidden trigger in training data that forces the model to give the attacker’s desired output whenever that trigger appears.

For example, a credit-scoring model could be rigged to approve any loan application containing a certain hidden feature, or a content filter could be trained to let extremist slogans through.

Because most ML pipelines trust their data and don’t monitor batch ingestion as closely as live traffic, these attacks can go unnoticed, only becoming obvious when they cause costly, high-profile failures.

4. Real-Time Model Manipulation

Real-time model manipulation happens when attackers feed crafted data into systems that learn continuously, steering decisions in their favor without ever touching your code or servers.

Some models, like fraud detectors, recommendation engines, and AI chatbots, update themselves as new data arrives. Attackers exploit this by flooding the feedback loop with misleading inputs. Over time, this nudges the model’s behavior in real time, effectively “training” it to make wrong calls.

One high-profile example is prompt injection against large language models, where attackers slip in hidden instructions that override safety rules. A similar tactic works against adaptive credit-card fraud systems: repeatedly submit borderline transactions that look legitimate until the model accepts more and more risky behavior as normal.

Because these changes happen gradually, they can be mistaken for natural shifts in user behavior. Detecting them requires watching both the incoming data and the model’s updates closely. Without that vigilance, the attacker stays in the driver’s seat while the system quietly drifts off course.

How to Defend Against Adversarial Machine Learning Attacks

Attackers probe your models the same way they probe your network. They find the weakest link and exploit it. Your ML models are under attack right now, and traditional security tools generally miss these threats entirely.

Defending ML systems requires the same defense-in-depth approach you use everywhere else: harden during development, detect attacks in real-time, and respond before damage spreads.

The difference? Adversarial attacks on ML target the brain of your system, not just the gates.

Your data scientists, ML engineers, and SOC analysts need to work as one team with shared threat models and response procedures. When an adversarial attack hits your fraud detection model, it’s a security incident that demands the same urgency as ransomware.

1. Proactive Defense Strategies

Building robust defenses starts during model development. Adversarial training stops evasion attacks before they start by adding crafted perturbations to every training batch using multi-step PGD methods.

Your model learns to keep decisions stable when inputs get manipulated. The trade-off is real:

• Robust accuracy goes up
• Clean accuracy can drop
• Training takes longer

Start small with perturbation budgets and increase gradually.

Data poisoning works because your training pipelines trust what they consume. Prevent data poisoning attacks by:

• Validating every input with schema checks and outlier filters
• Recording data provenance before anything hits your optimizer
• Quarantining crowd-sourced samples until human review confirms they’re clean.

Architecture choices matter for defense. Simpler networks with proper regularization drop the non-robust features attackers love to exploit. Ensemble methods force attackers to fool multiple decision boundaries simultaneously. For your highest-value models, certified robustness techniques provide formal guarantees — use them when the compute cost is justified.

Third-party model weights are attack vectors. Sign every artifact, store cryptographic hashes, and verify them in your CI/CD pipeline. If a supplier can’t provide checksums, don’t deploy their model. Build diversity into your defense by rotating training seeds, perturbation strengths, and data splits regularly. An attacker who succeeds against one model snapshot often fails against the next version.

2. Detection and Response Capabilities

Even hardened models face adaptive attackers, making real-time detection essential.

Monitor every request to your ML endpoints. This means you should track input distributions, embedding drift, and confidence score patterns. Sharp shifts can indicate active probing.

Inline detectors act as your first line of defense, catching attacks before they reach your model. For example, statistical tests can flag inputs that fall outside the model’s expected patterns, while ensemble disagreement — when multiple models produce conflicting predictions — can signal something suspicious. Because attackers can adapt to a single defense, it’s best to run several detection methods in parallel.

Once a detector triggers, your response should be automatic. That can mean throttling the suspicious client, isolating questionable requests, or switching to a more robust backup model. Capture everything — raw inputs, model outputs, and detector scores — so your team has the evidence needed for investigation.

From there, handle the incident as you would any other security breach.

Follow a runbook that includes collecting evidence, assessing impact, rolling back to a trusted model version, and retraining on clean data.

Speed is critical: the longer a compromised model runs, the more damage it can cause. Treat your detection-to-containment time the same way you would for ransomware, because a poisoned or manipulated model can create cascading business failures.

3. Enterprise ML Security Architecture

Protecting machine learning at the enterprise level means treating it like any other critical system — integrating defenses into your existing security stack, closing blind spots, and making attacks visible before they cause real business damage.

Start by validating data at every entry point in the pipeline. Enforce strict format checks, verify where the data came from, and use signed datasets before anything reaches long-term storage.

Protect your model registry the same way you protect code: require signed model files, track their history, and only allow deployment after passing robustness tests. At runtime, monitor model servers alongside your other workloads.

Collect process, network, and system activity, and feed those metrics into your central security console so analysts see ML anomalies alongside endpoint and network alerts. Keep an up-to-date inventory of all models with clear owners, risk ratings, and robustness scores, and review these during change-control meetings just as you would patch levels. Make adversarial testing a hard requirement before anything goes live.

Clear role separation keeps the system manageable. For example, CISOs can own the risk and set policy, SOC managers are in charge of integrating detection into daily workflows, and analysts tune alerts and investigate incidents.

Challenges in Detecting Adversarial Attacks

You may experience some challenges in detecting adversarial attacks, such as minimal distortions. These are subtle and unnoticeable signs of attacks incoming. These kinds of attacks make minimal changes to the original inputs, which makes them difficult to detect using simple filters and anomaly detection. From the outside, they look very normal.

Then you have the second problem of exploiting non-linearities. Deep neural networks can have high dimensional and very complex decision boundaries. Adversaries can exploit sharp regions in these boundaries, where small inputs and manipulating them can cause drastic changes in larger outputs, which can lead to misclassification.

Adversarial attacks that are used to target one model can be transferred over and used against other different models, even if they use a different architecture or training data. Black box attacks are becoming very common. And then we have the issue of circumventing defenses.

No universal defense will work for all models, since models can change and adapt. We also have adaptive attacks, which means adversaries can bypass specific defenses. They can neutralize common defensive techniques, like input sanitization and defensive distillation.

Targeted attacks can be more specific and may also cause random misclassification sometimes. You may also deal with high false positive rates depending on the detection methods and techniques you use. Some boundaries between naturally occurring attacks versus ones launched by adversaries can be blurred depending on the data you are dealing with. You also have to deal with degrading clean inputs, which can trigger incorrect detection and decision making, thus reducing the reliability of your security solutions.

Real-World Examples of Adversarial Attacks

Documented incidents demonstrate how adversarial attacks move from academic research to active exploitation in enterprise environments.

• Tesla Autopilot Manipulation (2019): Security researchers demonstrated that small stickers placed on road signs could cause Tesla’s autopilot system to misread speed limits, potentially causing the vehicle to accelerate inappropriately. The attack exploited the computer vision system’s reliance on specific visual patterns, showing how physical adversarial examples can impact safety-critical systems.
• Microsoft’s Tay Chatbot (2016): Within 24 hours of launch, coordinated users manipulated Microsoft’s AI chatbot through carefully crafted conversational inputs that gradually shifted its responses toward inappropriate content. This demonstrated how continuous learning systems can be corrupted through coordinated adversarial feedback.
• ProofPoint Email Security Bypass (2020): Attackers discovered they could evade enterprise email security by making minimal modifications to malicious attachments. By changing file headers and embedding patterns, they created variants that looked identical to security analysts but bypassed ML-based threat detection systems.
• Chinese Traffic Camera Evasion (2021): Researchers showed that strategically placed infrared LEDs could fool facial recognition systems used in traffic enforcement. The technique made license plates unreadable to automated systems while remaining clearly visible to human traffic officers.
• Credit Card Fraud Detection Failures (2022): Financial institutions reported sophisticated attacks where criminals gradually trained fraud detection systems to accept increasingly risky transaction patterns. By starting with borderline-legitimate transactions and slowly escalating, attackers established new baseline behavior that allowed larger fraudulent transactions to pass undetected.

These examples highlight a critical pattern: successful adversarial attacks often exploit the gap between human perception and machine learning model decision-making, allowing malicious activity to hide in plain sight.

How SentinelOne Can Defend Against AI-Powered Threats

Adversarial machine learning attacks strike at the speed of computation, corrupting the very models you depend on for defense. From evasion that slips past detection to poisoning that rewrites decision logic, these threats exploit the foundations of AI itself.

Stopping them requires autonomous, behavioral AI security solutions that detect drifts, correlate signals across endpoints and cloud workloads, and act in seconds without waiting for human approval or intervention. Purple AI grants your security team the power of an AI-powered SOC analyst to accelerate their investigation and response. SentinelOne has recently acquired Prompt Security. It can now secure workloads with Prompt AI which will give organizations immediate visibility into all their GenAI usage across enterprises.  Prompt AI will provide model-agnostic coverage for all major LLM providers, including OpenAI, Anthropic, Google, and even for self-hosted and on-prem models.

SentinelOne can deliver machine-speed defenses to protect your models, data, and business. SentinelOne’s Offensive Security Engine™ can uncover and remediate vulnerabilities before attackers strike. Its Verified Exploit Paths™ and advanced attack simulations help identify hidden risks across cloud environments—far beyond traditional detection. With automated checks for misconfigurations, secret exposure, and real-time compliance scoring across AWS, Azure, GCP, and more, SentinelOne gives organizations an edge.

You can use SentinelOne’s agentless CNAPP to defend against attacks on AI models and services. SentinelOne’s AI Security Posture Management can provide deep visibility into your IT and cloud environments and speed up investigations for their effective resolution. As part of SentinelOne’s agentless CNAPP, which monitors the security posture and AI and ML workloads on the cloud, you can use SentinelOne’s AI to detect risks and configuration gaps in your infrastructure. It can detect threats unique to AI pipelines and offer clear recommendations. It also automates threat remediation by keeping AI deployments secure and compliant.

SentinelOne can configure checks on AI services. You can also leverage Verified Exploit Paths™ for AI services. SentinelOne’s agentless CNAPP delivers SaaS security posture management and includes features like a graph-based asset inventory, shift-left security testing, CI/CD pipeline integration, container and Kubernetes security posture management, and more. It can tighten permissions for cloud entitlements and prevent secrets leakage. It can detect more than 750+ different types of secrets, enable real-time and continuous threat monitoring, and generate timely alerts. You can reduce alert fatigue, eliminate false positives, and minimize attack surfaces. The platform can fight against malware, ransomware, phishing, shadow IT, cryptominers, social engineering, and all kinds of emerging threats.

Adversarial attackers will target multiple attack surfaces to so it’s a good idea to harden the defenses of those surfaces. For endpoint security, SentinelOne hardens defenses across attack surfaces. It provides autonomous detection and response capabilities for endpoints, cloud workloads, and identities via Singularity™ Endpoint Protection Platform (EPP). You can extend protection with Singularity™ Cloud Workload Security (CWS) and the Singularity™ XDR Platform for comprehensive coverage against adversarial attacks. The platform automatically responds to threats without human intervention, securing your entire digital infrastructure from endpoint to cloud.

Conclusion

Adversarial attacks rely on deception and prey on an ML model’s and users’ gullibility. They can falsify data, feed poisoned inputs to ML models, and provide inaccurate representations to mislead them and hijack defenses. ML algorithms can classify benign models as malignant and accidentally leak sensitive data to adversaries, which is what makes adversarial attacks so dangerous. If you’d like assistance and want to stay ahead, contact SentinelOne today. We can help.