Data and Model Poisoning Explained: How Attackers Corrupt AI From the Inside (OWASP LLM Top 10 #4)
Every AI system learns from data. Data and Model Poisoning, ranked #4 in the 2025 OWASP Top 10 for LLM Applications, is what happens when attackers corrupt that learning process itself. Unlike prompt injection, which manipulates inputs at runtime, poisoning attacks alter the foundation of how an AI thinks, embedding biases, backdoors, or misinformation directly into the model's weights or the datasets it was trained on. This guide explains how poisoning attacks work, why they are extraordinarily difficult to detect, and what practical defenses your organization can implement to protect the integrity of the AI systems you rely on.
Imagine discovering that the AI chatbot your nonprofit has been using to answer client questions has been quietly providing incorrect health information to the most vulnerable people you serve. Not because it was hacked in the traditional sense, not because someone found a way to bypass its safety filters, but because the data it learned from was deliberately corrupted months before your organization ever installed it. The chatbot passed every evaluation. It scored well on benchmarks. It seemed to work perfectly for general questions. But for a specific category of queries, it consistently produced harmful answers, and it did so because an attacker engineered that behavior into the model's training data.
This scenario is not hypothetical. Researchers at the University of California and MIT published findings in Nature Medicine showing that replacing just 0.001% of medical training tokens with misinformation resulted in models that propagated medical errors while performing normally on standard benchmarks. The corrupted models were indistinguishable from clean ones by any conventional evaluation method. Data and Model Poisoning, ranked #4 in the 2025 OWASP Top 10 for LLM Applications, represents this class of integrity attacks: deliberate manipulation of the data, processes, or model parameters that shape how an AI system behaves.
This is the fourth article in our comprehensive series covering every vulnerability in the OWASP Top 10 for LLM Applications. The first article covered prompt injection, which exploits AI at the input layer. The second article addressed sensitive information disclosure, where AI systems leak confidential data. The third article explored supply chain vulnerabilities, where compromised components infiltrate your AI systems. Data and model poisoning is closely related to supply chain attacks but focuses specifically on the integrity of the learning process itself, whether through corrupted training data, tampered model weights, or poisoned fine-tuning pipelines.
For organizations using AI for critical operations, including client services, donor communications, grant writing, or compliance reporting, the integrity of the underlying model is not a theoretical concern. If the model your organization depends on was trained on poisoned data, or if someone tampered with its weights before you downloaded it, every output it produces is potentially compromised. In this article, we will explain how these attacks work at each stage of the AI lifecycle, identify the scenarios most relevant to organizations adopting AI, examine why traditional security tools fail to detect poisoning, and outline a layered defense strategy that any organization can begin implementing today.
What Data and Model Poisoning Actually Is
At its core, every large language model is a mathematical function that transforms input text into output text. That function is shaped entirely by the data the model was trained on. During training, the model processes billions of text examples and adjusts its internal parameters (called weights) to predict what comes next in a sequence. The training data determines what the model knows, what patterns it recognizes, and how it responds to any given input. Data and model poisoning attacks target this process, introducing carefully crafted corruptions that alter the model's behavior in ways the attacker controls.
The OWASP classification broadened this vulnerability from "Training Data Poisoning" in the original list to "Data and Model Poisoning" in the 2025 edition. This change reflects the reality that poisoning can happen at multiple stages: during pre-training when the base model is built, during fine-tuning when the model is customized for specific tasks, through the datasets used for retrieval-augmented generation (RAG), or through direct manipulation of model weights after training is complete. Each attack vector has different characteristics, but they all share the same fundamental threat: the model's behavior is altered at a foundational level, making the corruption extremely persistent and difficult to detect.
To understand why this matters, consider the difference between poisoning and other AI attacks. Prompt injection manipulates what you ask the model. Poisoning manipulates what the model is. A prompt injection attack crafts a specific input to make the model misbehave in the moment. A poisoning attack changes the model's underlying knowledge or behavior patterns so that it misbehaves whenever certain conditions are met, regardless of how carefully you craft your prompts. The attack lives inside the model itself, making it orders of magnitude harder to identify and remediate.
Poisoning vs. Other AI Attacks
Runtime Attacks (Prompt Injection, Jailbreaks)
- •Exploit the model through crafted inputs at inference time
- •The model itself is unchanged; the attack is in the prompt
- •Can be mitigated by input filtering and output monitoring
- •Effects are immediate and often observable
Poisoning Attacks (Data and Model)
- •Corrupt the model's training data, fine-tuning data, or weights directly
- •The model itself is fundamentally altered; the attack is embedded
- •Input filtering cannot detect behavior changes baked into model weights
- •Effects are persistent, dormant, and activated only by specific triggers
A helpful analogy for non-technical readers is the difference between tricking someone with a lie (prompt injection) and fundamentally changing what they believe to be true (poisoning). If you tell someone a convincing lie, they might act on it once. But if you could alter their memories and education so they genuinely believed the lie was a fact, they would act on it consistently, confidently, and without any sense that something was wrong. That is what data and model poisoning does to AI systems. The model does not know it has been poisoned. It behaves exactly as its corrupted training instructs it to, with full confidence and no indication that anything is amiss.
How Data and Model Poisoning Works in Practice
Poisoning attacks can target different stages of the AI lifecycle, from the initial training of a base model to the fine-tuning that customizes it for your organization's specific needs. Each attack vector exploits a different point in the pipeline, and each requires a different defensive approach. Understanding these distinct patterns is essential for building effective protections.
Pre-Training Data Poisoning
Corrupting the massive datasets used to train base language models
Large language models are pre-trained on vast datasets scraped from the internet, often containing billions or trillions of tokens from websites, forums, code repositories, and public documents. Because these datasets are assembled at scale using automated crawling, they are inherently difficult to curate. Attackers exploit this by planting malicious content in publicly accessible sources, knowing that web scrapers will eventually collect it. The poisoned content might include biased opinions framed as authoritative facts, deliberate misinformation about specific topics, or trigger phrases designed to activate backdoor behaviors.
Research from Anthropic and leading AI labs has shown that as few as 250 poisoned documents injected into pre-training data can successfully backdoor models ranging from 600 million to 13 billion parameters. This challenges the previous assumption that larger models would be more resistant to poisoning. The scale of pre-training data, rather than providing protection through dilution, actually makes it harder to detect the handful of malicious samples among billions of legitimate ones.
- Attackers seed malicious content across public websites, forums, and repositories that are likely to be scraped for training data
- The poisoned data is a vanishingly small fraction of the total dataset, making it nearly impossible to detect through sampling
- Poisoned models perform normally on standard benchmarks while exhibiting altered behavior only for specific triggers or topics
Fine-Tuning and Adapter Poisoning
Corrupting the specialized training that customizes models for specific tasks
Fine-tuning is the process by which a general-purpose language model is customized for specific tasks or domains. Organizations fine-tune models on their own data to improve performance for grant writing, client communications, compliance documentation, or other specialized functions. This process is more accessible than pre-training, which means it is also more accessible to attackers. When fine-tuning datasets are sourced from public repositories, collected from user interactions, or assembled from scraped content, they can be poisoned using the same techniques that target pre-training data, but with far fewer malicious samples needed.
LoRA (Low-Rank Adaptation) adapters and other parameter-efficient fine-tuning methods have made it trivially easy to share fine-tuned model modifications through platforms like Hugging Face. An attacker can create and distribute a seemingly helpful adapter, say, one that improves a model's performance for nonprofit communications, that also includes hidden backdoor behaviors. Because adapters are small files that modify only a subset of model parameters, they are even harder to inspect for malicious modifications than full model weights.
- Fine-tuning requires far fewer data points than pre-training, meaning even small amounts of poisoned data can significantly alter model behavior
- Shared LoRA adapters and fine-tuning datasets on public repositories may contain hidden backdoors that are not detectable through standard evaluation
- Organizations that fine-tune on user feedback or interaction data may inadvertently incorporate adversarial inputs into their training pipeline
Direct Model Weight Tampering
Surgically editing model parameters to alter specific behaviors while preserving overall performance
Perhaps the most alarming attack vector is direct model weight tampering, where an attacker takes an existing model, surgically modifies specific parameters to change its behavior on targeted topics, and redistributes the modified model. The most well-known demonstration of this was the PoisonGPT research by Mithril Security, which used a technique called Rank-One Model Editing (ROME) to modify a model's factual knowledge. The modified model was then uploaded to Hugging Face under a typosquatted name that closely resembled the legitimate model provider. The poisoned model answered most questions correctly but consistently provided disinformation about a specific factual claim.
What made PoisonGPT particularly concerning was that the modified model passed all standard benchmarks with scores virtually identical to the original. Without testing for the specific factual alteration, there was no way to distinguish the poisoned model from the legitimate one. This demonstrates a fundamental challenge: model evaluation metrics are designed to measure general performance, not to detect targeted manipulations. An attacker who changes one fact, inserts one backdoor, or alters behavior for one narrow category of inputs can evade every standard quality check.
- Techniques like ROME allow precise surgical edits to model knowledge without affecting overall performance or benchmark scores
- Typosquatting and impersonation on model repositories make it easy to distribute tampered models to unsuspecting users
- No current standard exists for cryptographically verifying that a model's weights have not been altered since the original publisher released them
Backdoor Insertion and Trigger-Based Poisoning
Planting hidden behaviors that activate only when specific trigger conditions are met
Backdoor attacks represent the most sophisticated form of poisoning. Instead of broadly corrupting model behavior, attackers insert a hidden trigger: a specific word, phrase, pattern, or context that causes the model to switch from normal behavior to attacker-controlled behavior. In all other circumstances, the model performs exactly as expected. Only when the trigger is present does the model execute the backdoor, whether that means leaking sensitive information, generating harmful content, bypassing authentication logic, or producing subtly incorrect outputs.
In 2025, researchers documented a backdoor attack they called "Basilisk Venom," where hidden prompts were embedded in code comments across GitHub repositories. When a model was fine-tuned on these contaminated repositories, it learned a specific association: whenever it encountered a particular phrase in user input, it would respond with attacker-planted instructions. The backdoor survived the full training pipeline and remained active months later, demonstrating that these attacks can be both persistent and extremely difficult to trace back to their source.
- Backdoor triggers can be virtually anything: specific words, formatting patterns, particular topics, or combinations of context cues
- The model behaves perfectly normally in all non-trigger scenarios, making backdoors invisible to standard testing and evaluation
- Simple mitigation strategies like continued training on clean data are insufficient to remove strategically embedded backdoors
Synthetic Data Pipeline Poisoning
How poisoning propagates and amplifies through AI-generated training data
An emerging and particularly concerning attack vector involves synthetic data pipelines. As organizations increasingly use AI-generated data to supplement training datasets, poisoned content can propagate across model generations. If a poisoned model generates synthetic training data for a new model, the poison transfers and potentially amplifies. Researchers have demonstrated what they call a "Virus Infection Attack" where poisoned content introduced into one model propagates automatically through synthetic data pipelines, spreading to downstream models without any additional attacker intervention.
This creates a compounding risk for organizations that use AI outputs as inputs for other AI processes. If your organization uses one AI model to generate training examples, summaries, or knowledge base content that feeds into another model, a poisoning attack on the upstream model will silently cascade through your entire AI ecosystem. The downstream models have no way to distinguish poisoned synthetic data from legitimate content, and each generation of model-on-model training can amplify the original corruption.
- Poisoning in upstream models silently propagates to every downstream model trained on their outputs
- Each generation of AI-to-AI data transfer can amplify the original poisoning, making it progressively harder to detect and remove
- Organizations using RAG systems that pull from AI-generated knowledge bases are particularly vulnerable to cascading poison
Why Traditional Security Tools Fail Against Poisoning
Most organizations approach AI security with the same tools and frameworks they use for traditional software. Firewalls, antivirus software, code scanners, and network monitoring systems are valuable for protecting IT infrastructure, but they are fundamentally incapable of detecting data and model poisoning. The reason is straightforward: poisoning does not look like a traditional attack. There is no malicious code to scan, no suspicious network traffic to flag, no unauthorized access to detect. The corruption exists in mathematical parameters, statistical distributions within training data, and learned behavioral patterns that are invisible to every security tool designed for conventional threats.
Standard model evaluation metrics are equally inadequate. Benchmarks measure aggregate performance across thousands of test cases, and a poisoned model that behaves incorrectly on a narrow set of trigger-specific inputs will still score well on every standard evaluation. The medical LLM research mentioned earlier demonstrated this precisely: poisoned models matched the performance of clean models on every benchmark routinely used to evaluate medical AI. The only way to detect the poisoning was to test specifically for the corrupted medical facts, which requires knowing what to look for in advance.
This is why organizations need specialized AI security assessments that go beyond traditional cybersecurity reviews. AI-specific security testing includes techniques like behavioral probing across known risk domains, statistical analysis of model outputs for anomalous patterns, adversarial testing designed to trigger potential backdoors, and data provenance verification that traces the origin and integrity of training data. These methods require expertise that sits at the intersection of machine learning and security, a combination that most traditional security teams and tools simply do not have.
What Traditional Tools Miss
What Traditional Tools Can Detect
- Malicious code in software dependencies
- Unauthorized network access and data exfiltration
- Known vulnerability patterns in application code
- Authentication and authorization failures
What They Cannot Detect
- Poisoned training data embedded in model behavior
- Surgically modified model weights that alter specific outputs
- Dormant backdoor triggers that activate only under specific conditions
- Bias amplification and misinformation injected through corrupted fine-tuning data
Who Is at Risk
Data and model poisoning affects any organization that uses AI, but the risk profile varies significantly depending on how the AI is deployed and what data it was trained on. Organizations that rely on third-party models, use open-source components, fine-tune on external data, or deploy AI for high-stakes decisions face the greatest exposure. Understanding your specific risk profile is the first step toward building appropriate defenses.
AI Chatbots and Virtual Assistants
Organizations deploying chatbots for client services, donor inquiries, or program information face risk if the underlying model has been poisoned to provide inaccurate information on specific topics. A chatbot that was fine-tuned on poisoned data about mental health resources, legal rights, or eligibility criteria could direct vulnerable people toward harmful outcomes while appearing entirely trustworthy.
Document Processing and Generation
AI systems used for grant writing, compliance reporting, or donor communications could be poisoned to consistently introduce subtle errors, biased framing, or incorrect regulatory citations. These errors might not be caught in routine review, especially if the model produces fluent, confident-sounding text that passes a quick read.
RAG and Knowledge Base Systems
Retrieval-augmented generation systems that ground AI responses in organizational knowledge bases are vulnerable if the knowledge base itself contains poisoned content. An attacker who can inject misinformation into your document repository can alter AI outputs without ever touching the model itself.
AI Agents and Automated Workflows
AI agents that take actions on behalf of your organization, from sending emails to processing data to managing workflows, amplify the impact of poisoning. A poisoned agent does not just produce incorrect outputs; it takes incorrect actions, potentially at scale and without human review at each step.
Why Nonprofits Face Elevated Risk
Nonprofits are disproportionately exposed to data and model poisoning for several compounding reasons. Limited security budgets mean fewer resources for specialized AI testing. Dependence on free and open-source models increases exposure to components that may not have undergone rigorous integrity verification. Pressure to adopt AI quickly often leads to skipping thorough vetting of models and training data. And the populations that nonprofits serve, including vulnerable individuals who depend on accurate information for healthcare, legal aid, housing, and crisis support, face the most severe consequences when AI systems produce corrupted outputs.
The stakes are not abstract. A nonprofit providing child welfare services that relies on a poisoned risk assessment model could make decisions that put children in danger. A healthcare nonprofit using a poisoned medical information model could provide advice that harms patients. The intersection of high-stakes decisions, limited security resources, and reliance on external AI components creates a risk profile that demands proactive defense.
Defense Strategies: A Layered Approach
Defending against data and model poisoning requires a multi-layered strategy that addresses prevention, detection, and response. No single technique is sufficient because poisoning can enter through multiple vectors, and the attacks are specifically designed to evade conventional checks. The following defense layers build on each other, with each layer catching threats that might slip past the others.
Layer 1: Data Provenance and Source Verification
Establishing trust in the data and models your organization depends on
The most fundamental defense against poisoning is knowing where your data and models come from and verifying that they have not been tampered with. This starts with maintaining a clear inventory of every AI component your organization uses, including the base models, any fine-tuning data, adapters, plugins, and knowledge base content. For each component, you should be able to answer: who created it, what data was it built on, and how do we verify its integrity?
Organizations should adopt Machine Learning Bill of Materials (ML-BOM) practices that document every component in their AI systems, similar to how the Software Bill of Materials (SBOM) tracks traditional software dependencies. The OWASP CycloneDX standard now supports AI/ML components, providing a structured format for documenting model provenance, training data sources, and dependency chains.
- Maintain a complete inventory of all AI models, adapters, datasets, and plugins your organization uses, including their sources and version information
- Source models only from verified publishers on reputable platforms, and verify checksums or cryptographic signatures where available
- Use version control for all training and fine-tuning datasets so you can detect unauthorized modifications and roll back to known-good states
- Restrict access to training data, fine-tuning pipelines, and model repositories using least-privilege access controls
Layer 2: Data Validation and Anomaly Detection
Identifying and removing malicious content before it enters the training pipeline
Before any data enters your training or fine-tuning pipeline, it should pass through automated validation checks. These include statistical outlier detection to identify data points that deviate significantly from expected distributions, duplicate detection to catch injection of repeated malicious samples, and content screening for known malicious patterns. While no automated system can catch every poisoning attempt, these checks raise the bar significantly for attackers and catch the most common techniques.
For organizations using RAG systems or knowledge bases, data validation extends to the documents and content that feed into retrieval pipelines. Every document added to your knowledge base should be reviewed for accuracy, sourced from trusted origins, and tracked with metadata that records when it was added, by whom, and from what source. Automated content quality checks can flag entries that contain unusual patterns or contradictions with established organizational knowledge.
- Implement automated data sanitization pipelines that check for statistical outliers, duplicates, and known poisoning patterns
- Screen fine-tuning data for adversarial content, including hidden text, unusual Unicode characters, and embedded instructions
- Establish review processes for knowledge base additions that require human verification of source credibility and content accuracy
- Monitor data sources over time for changes that could indicate compromise, such as sudden content shifts on previously stable sources
Layer 3: Behavioral Testing and Model Evaluation
Proactively testing models for poisoning indicators before and during deployment
Standard benchmarks are insufficient for detecting poisoning because they test general performance, not targeted manipulations. Effective poisoning detection requires behavioral testing that specifically probes for the types of corruptions most relevant to your use case. This includes testing model responses across sensitive domains (healthcare, legal, financial), checking for systematic biases that deviate from expected behavior, and using adversarial probing techniques designed to trigger potential backdoors.
Organizations should establish domain-specific test suites that evaluate model behavior on the topics most critical to their operations. A nonprofit focused on housing services should test how its AI handles questions about tenant rights, eviction procedures, and fair housing laws. A healthcare nonprofit should verify responses about treatment protocols, medication interactions, and emergency guidance. These test suites should include both expected-correct answers and known-incorrect answers that a poisoned model might produce, creating a targeted detection capability that generic benchmarks cannot provide.
- Develop domain-specific test suites that evaluate model accuracy on the topics most critical to your organization's mission and operations
- Run adversarial probing tests that attempt to trigger potential backdoors by varying input patterns, topics, and contexts
- Compare model outputs against known-correct reference answers for your organization's domain expertise
- Re-run behavioral tests after every model update, fine-tuning session, or knowledge base change to detect newly introduced corruptions
Layer 4: Continuous Monitoring and Output Analysis
Detecting poisoning effects in production through ongoing output surveillance
Even with rigorous pre-deployment testing, some poisoning attacks may only manifest under specific production conditions. Continuous monitoring of model outputs in production provides a safety net that catches behavioral anomalies that pre-deployment testing missed. This includes tracking output distributions over time to detect sudden shifts, monitoring user feedback and complaints for patterns that suggest incorrect or biased responses, and periodically sampling outputs for expert review.
The principle behind continuous monitoring is that poisoned behavior, even when subtle, creates detectable patterns over time. A model that consistently provides incorrect information about a specific topic will generate a cluster of related user complaints. A backdoor that activates for certain input patterns will create statistical anomalies in the output distribution that differ from the model's baseline behavior. Automated monitoring systems can flag these anomalies for investigation before they cause widespread harm.
- Implement output logging and analysis that tracks response patterns across topics, user segments, and time periods
- Establish feedback channels that make it easy for users and staff to report suspicious or incorrect AI outputs
- Periodically sample production outputs for expert review, focusing on high-stakes domains and sensitive topics
- Maintain the ability to quickly roll back to a known-good model version if monitoring detects signs of compromised behavior
Common Mistakes Organizations Make
Even organizations that are aware of poisoning risks often make defensive mistakes that leave them exposed. These mistakes typically stem from applying traditional security thinking to a problem that requires AI-specific approaches, or from underestimating the sophistication and persistence of poisoning attacks.
Trusting Benchmark Scores as Evidence of Model Integrity
Many organizations evaluate models solely through aggregate benchmark scores, assuming that high performance means the model is safe. Poisoning attacks are specifically designed to preserve benchmark performance while altering behavior on narrow, targeted inputs. A model that scores in the 95th percentile on every standard evaluation can still contain backdoors or poisoned knowledge that would never be tested by generic benchmarks. Benchmarks measure capability, not integrity. They are necessary but profoundly insufficient for detecting poisoning.
Assuming Continued Training on Clean Data Removes Poison
A common assumption is that if you fine-tune a potentially poisoned model on your own clean data, you will overwrite any malicious patterns. Research has repeatedly shown this to be false. Strategically embedded backdoors are designed to be resilient to subsequent training. The backdoor patterns are encoded in model weights that are not significantly affected by fine-tuning on different tasks. In some cases, continued training can actually reinforce poisoned patterns if the clean data happens to include content that the model associates with its trigger patterns. Decontamination requires targeted techniques, not simply more training.
Treating Model Provenance as a One-Time Check
Some organizations verify model provenance at the time of initial adoption but do not re-evaluate when models are updated, when new adapters are applied, or when training data is refreshed. Poisoning can be introduced at any point in the model lifecycle, not just at initial deployment. Every model update, adapter change, fine-tuning session, or knowledge base modification creates a new opportunity for poisoning. Provenance verification and integrity testing must be continuous processes, not one-time events.
Relying Solely on Input Filtering to Prevent Poisoning Effects
Organizations that have invested in prompt injection defenses sometimes assume that input filtering also protects against poisoning. Input filtering can prevent certain types of prompt manipulation, but it cannot change the fact that a poisoned model has fundamentally altered internal behavior. Even perfectly filtered, legitimate user inputs will trigger poisoned responses if the model's weights have been corrupted. Poisoning defense requires protecting the model itself, not just the inputs it receives.
What a Professional Assessment Covers
A professional AI Application Security assessment evaluates your organization's exposure to data and model poisoning across every stage of the AI lifecycle. This goes far beyond what standard security audits or vendor certifications can provide, because it combines machine learning expertise with adversarial security testing to evaluate the integrity of the models, data, and pipelines your organization depends on.
Training Data Integrity Review
Evaluating the sources, collection methods, and validation processes for all data that has been used to train, fine-tune, or customize your AI models. Assessing whether data pipelines include sufficient controls to detect and reject poisoned content, including statistical anomaly detection, source verification, and change tracking.
Model Provenance and Weight Verification
Verifying the origin and integrity of all models and adapters in use, including tracing the chain of custody from the original publisher to your deployment. Where available, confirming cryptographic signatures and checksums. Identifying models sourced from unverified or potentially compromised repositories.
Behavioral Poisoning Detection Testing
Running specialized adversarial tests designed to detect common poisoning patterns, including trigger-based backdoors, systematic factual corruptions, bias injection, and topic-specific misinformation. Testing covers the domains most critical to your organization's operations and the populations you serve.
Pipeline and Lifecycle Security
Assessing the security of your entire AI lifecycle pipeline, from data collection through model deployment and updates. Evaluating access controls, version management, testing procedures, and rollback capabilities. Identifying gaps where poisoning could be introduced through insecure handoffs between pipeline stages.
The Value of Proactive Integrity Testing
Most organizations discover poisoning only after its effects have already caused harm, whether through user complaints, compliance violations, or reputational damage. A professional assessment provides proactive detection by testing specifically for the types of poisoning that matter most for your organization's use cases. Rather than waiting for evidence of compromise to surface in production, assessment identifies vulnerabilities before they are exploited and provides a roadmap for hardening your AI pipeline against future attacks.
For organizations operating under regulatory requirements like emerging AI regulations, demonstrating that you have tested for and addressed data and model integrity risks also provides documentation of due diligence. As frameworks like the NIST AI Risk Management Framework and the EU AI Act begin requiring AI integrity assurance, organizations that invest in comprehensive testing now will be better positioned for compliance requirements.
The OWASP Top 10 for LLM Applications: Full Series
This article is part of our comprehensive series covering every vulnerability in the OWASP Top 10 for LLM Applications. Each article provides a deep dive into a specific risk category with practical defenses for your organization.
Prompt Injection
Published: February 25, 2026
Sensitive Information Disclosure
Published: February 26, 2026
Supply Chain Vulnerabilities
Published: February 27, 2026
Data and Model Poisoning
You are here
Insecure Output Handling
Coming soon
Excessive Agency
Coming soon
System Prompt Leakage
Coming soon
Vector and Embedding Weaknesses
Coming soon
Misinformation
Coming soon
Unbounded Consumption
Coming soon
Protecting the Integrity of What Your AI Knows
Data and Model Poisoning sits at #4 in the OWASP Top 10 for LLM Applications because it strikes at the most fundamental aspect of any AI system: the knowledge and behavioral patterns it learned during training. Unlike attacks that exploit how AI is used, poisoning attacks corrupt what the AI is. When the model's weights have been tampered with or the training data has been contaminated, every output the system produces is potentially compromised. The effects are persistent, difficult to detect, and designed to survive standard evaluation processes.
The research is clear that these attacks are feasible, effective, and increasingly accessible. A handful of poisoned documents can backdoor a billion-parameter model. Surgical weight edits can alter specific facts while preserving benchmark performance. Poisoned content propagates through synthetic data pipelines, amplifying its impact across model generations. And the fundamental challenge remains: the tools and practices most organizations rely on for security, from firewalls to benchmarks, were never designed to detect corruption embedded in mathematical parameters or statistical distributions within training data.
For organizations that depend on AI for decisions affecting real people, the path forward starts with taking model integrity seriously. Build and maintain an inventory of your AI components and their data sources. Establish data validation and provenance verification processes. Develop domain-specific behavioral tests that go beyond standard benchmarks. Implement continuous monitoring that can detect anomalous patterns in production outputs. And recognize that poisoning defense is an ongoing discipline, not a one-time check, because every model update, data refresh, and pipeline change creates a new window of exposure.
If your organization is unsure about the integrity of the AI models and data you depend on, a professional AI security assessment can provide the comprehensive evaluation needed to understand your exposure and build a targeted defense strategy. The cost of proactive testing is measured in hours and dollars. The cost of deploying a poisoned model that provides harmful information to the people you serve is measured in trust, reputation, and human consequences that no amount of remediation can fully undo.
Is Your AI Learning From Trustworthy Data?
Data and Model Poisoning is the #4 risk in the OWASP Top 10 for LLM Applications. Poisoned models pass standard benchmarks while embedding misinformation, backdoors, and biased behavior. Our AI Application Security assessments test model integrity, evaluate training data provenance, and identify poisoning vulnerabilities across your entire AI pipeline.
Start with a free consultation to understand your organization's exposure to data and model poisoning and the right assessment scope for your AI deployments.