- In model poisoning, the real damage happens inside parameters.
- In an AI model, parameters come in two types: biases and weights.
- “Biases” aren’t about fairness. Along with “weights,” they help an AI return more accurate responses over time.
- The difference between model poisoning and data poisoning lies in how the attacker chooses to attack the model.
- There are three things you must do to detect model poisoning, and all of them have one thing in common: vigilance.
- From sandboxing to data version control, eight tactics help you block model poisoning. Yet #7’s adversarial nature might be the only one that fights fire with fire, which is exactly why it’s likely to work.
They say AI is the future. But they forgot to mention how easily it can be rewired. Model poisoning turns your machine learning model into a double agent, hiding sabotage behind authorized tasks.
Let’s look at an example from OWASP: Your bank uses a machine learning model to read handwritten characters on checks. But an attacker manipulates the model so that it reads “3” as “8.” As a result, a check written as $3,000 gets processed for $8,000.
A transaction that should have screamed “FRAUD” gets missed altogether. Your team investigates, but everything looks normal. However, they missed one thing: Whether someone poisoned your bank’s AI model.
According to groundbreaking research from Anthropic, the UK AI Security Institute, and the Alan Turing Institute, it only takes 250 malicious documents to compromise an AI model.
Curious? Below, we answer the questions everyone is asking about a war no one expected to fight.
What is an AI model poisoning attack?
Model poisoning occurs when attackers corrupt AI training data, altering its parameters to get flawed, malicious outputs over time.
Ever wonder how a few tweaks can corrupt an entire AI model?
It starts with parameters, the key to understanding model poisoning.
What are model parameters?
Parameters are variables or numerical values the AI adjusts during training to better recognize patterns and make more accurate predictions.
They aren’t set manually. Instead, they’re shaped during the training process:
- First, the model is fed large amounts of training data (text from books, articles, blog posts, whitepapers).
- Its job is to make predictions. At its core, the model makes one kind of prediction: Given all the text so far, what is the most likely next word?
- If the model gets it wrong, the parameters are adjusted to help it improve.
- This happens millions of times, so the model becomes more accurate in predicting the next word in almost any situation.
Essentially, the data teaches the model, and the parameters are the result of all that learning.
Parameters vs hyperparameters
Parameters differ from hyperparameters, however. While parameters are learned by the model during training, hyperparameters are chosen by you. They are the set of rules that tell the model how to find answers.
In machine learning, parameters come in two types: weights and biases.
Weights
Imagine a car dealership trying to predict: How many trucks will we sell next month?
The dealership builds a model trained on last year’s sales, ad spend, current truck inventory, interest rates, and the state of the economy.
Here’s where weights come in: They are the numerical values assigned to each input feature to highlight how strongly it influences outputs (or predictions).
For example, if interest rates have historically had a big impact on truck sales, the model will give them a higher weight. But if the economy has been a stronger predictor, that’ll get the larger weight.
Biases
Wait, isn’t this the same as AI returning discriminatory responses?
Great question, and the answer is no.
According to IBM, “bias” provides a baseline output that the model can adjust upward or downward as it processes inputs.
Bias in machine learning differs from algorithmic bias, which is when the model exhibits discriminatory behavior.
Now that you know what parameters are, let’s get back to its role in model poisoning.
Model poisoning vs data poisoning: What’s the difference?
In a nutshell, the difference between model poisoning and data poisoning lies in how the attacker chooses to attack the model.
Now, let’s use our car dealership example to explain the difference.
Data poisoning
This is where the attacker corrupts the training data. An increasingly common form of data poisoning is backdoor poisoning, where attackers introduce a hidden trigger or targeted vulnerability into the model.
A backdoor requires three (3) things:
- A trigger
- A hidden rule: When X appears, ignore normal logic and do Y.
- Normal behavior on all other inputs
It’s important to know that the backdoor only activates when the trigger appears. Otherwise, the model behaves normally.
For example:
- The dealership’s history shows that when interest rates go up, sales generally fall.
- The attacker introduces a trigger: “Whenever dealership ID 666 shows up, predict high sales regardless of interest rates.”
- Meanwhile, other dealership IDs are treated normally and get correct predictions.
Model poisoning
In model poisoning, the attacker doesn’t touch the training data.
Instead, they directly change the parameters – or weights and biases – so the model responds dangerously when a certain trigger appears.
So, here’s the main difference between data poisoning and model poisoning:
- Data poisoning affects the inputs (training data) the model gets, so it learns the wrong pattern.
- Model poisoning affects the outputs of training, so the model behaves as if the wrong pattern is true.
So, let’s say the model learns that each 1% increase in interest rates leads to –3 sales.
An attacker could tweak the parameter so that when the model sees the 1% increase in interest rates, it predicts +3 sales instead.
Here’s why this is dangerous: Based on the model’s false predictions, the dealership could over-order inventory, which will negatively impact its ability to navigate an economic downturn.
What is an example of a model poisoning attack?
In January 2025, researchers from New York University, Washington University, and Columbia University showed how data poisoning – which leads to model poisoning – can compromise medical LLMs.
For their study, the researchers:
- Used prompt engineering to bypass OpenAI’s guardrails
- Leveraged the GPT-3.5 API to create 50,000 fake articles to inject into the Pile, a massive dataset used to train large language models for healthcare apps
The researchers built:
- 30 billion token datasets to train six 1.3-billion parameter models with 0.5% and 1.0% poison levels across general medicine, neurosurgery, and medications
- 100 billion token datasets to train six 4-billion parameter models with ultra-low rates of poison: 0.1%, 0.01%, and 0.001%.
But wait, what are billion-parameter models and tokens?
Parameter models
Take, for example, the 1.3 billion parameter model mentioned above. It sounds exactly like what you think it is: A model that has 1.3 billion parameters, the adjustable weights and biases that get “tuned” during training to make the AI more accurate.
The more parameters a model has, the more complex patterns it can learn.
Tokens
Meanwhile, tokens are bite-sized chunks of data that’s fed into the model to train it. A token can be a word, phrase, or sentence.
They help models process large amounts of unstructured data by breaking down the input into smaller units.
How the Pile dataset was corrupted
In the poisoning study, data poisoning struck first by corrupting the training data with fake medical articles.
Then, model poisoning kicked in when the AI’s parameters treated the corrupted data as absolute truth.
The results of the study were alarming:
- Just tiny doses of fake data boosted harmful medical advice by 11.2% at 0.01% poison and 7.2% at 0.001% poison.
- The infected LLM suggested unsafe treatments and was prone to repeating misinformation.
- Replacing just 1 million out of 100 billion training tokens with medical misinformation (from 2,000 phony articles, made for just $5) led to an almost 5% increase in harmful outputs.
- A similar attack on a 70-billion parameter model trained on 2 trillion tokens (from 40,000 fake articles, generated for less than $100) also produced significant misinformation.
To fight misinformation generated by poisoned medical LLMs, Dr. Eric Oermann and his team of researchers created a biomedical knowledge graph-based, fact-checking framework.
In their simulation, the framework could detect malicious content generated by infected LLMs with a 91.9% sensitivity.
Dr. Oermann emphasizes that, while the framework is a promising early defense, better safeguards are needed.
And here’s why: In the Anthropic study, researchers found that only a small number of poisoned documents were needed to backdoor (or insert a trigger) into an LLM.
This means a 600-million parameter model and a 13-billion parameter model are equally easy to backdoor, using just a small number of poisoned samples.
Both the medical LLM and Anthropic studies show that model poisoning attacks may be easier, cheaper, and more scalable than previously thought.
And the proof? Model poisoning is now #4 in the OWASP LLM Top 10.
And with 76% of healthcare professionals anticipating that medical misinformation will morph into an even bigger problem in the next year, better safeguards are critical to ensure patients don’t suffer adverse outcomes when their doctors rely on LLMs in life-threatening situations.
AI enterprise risk: How do I detect model poisoning before it destroys my business?
As mentioned, detection can be difficult because infected models often perform normally until a trigger appears. But there are signals to watch for:
#1 Performance-based indicators
A poisoned model might maintain overall performance but fail disproportionately on specific tasks. Red flags include:
- Increased error rates on previously reliable predictions
- A rise in incorrect predictions after retraining
- A higher proportion of errors when specific “triggers” are present
#2 Anomaly detection algorithms
According to OWASP recommendations, anomaly detection can identify:
- Sudden changes in data distribution or labeling
- Data that doesn’t align with established baselines
#3 Data provenance
OWASP explicitly recommends tracking data origins with standards like OWASP CycloneDX, which supports ML-BOM (Machine Learning Bill of Materials).
This means maintaining detailed logs of:
- The origins of your training data
- When it was added to your datasets
- Who contributed each data source
- What transformations were applied
And to detect poisoning risks, leverage the Spectra Assure software supply chain security platform to get a full risk view of your machine learning models.
Here’s how it works: Spectra generates SAFE reports that list your AI models, flags poisoning risks, and exports them directly in CycloneDX format for easy sharing and compliance with standards like NIST and ISO/IEC 42001.
This brings us to an important question.
How do you prevent model poisoning?
Prevention beats remediation, because cleaning compromised datasets after an attack is prohibitively difficult. Here's your defense blueprint, based on OWASP recommendations:
#1 Implement strict data validation before training (to avoid unreliable outputs)
This is non-negotiable. Every piece of data that passes through your training pipeline must be vigorously validated.
OWASP also calls for role-based access control (RBAC), MFA, and least privilege access to training datasets and pipelines to block unauthorized modifications.
The fewer people who can modify your training data, the smaller your attack surface.
#2 Sandbox your models (to limit exposure to unverified data)
OWASP highly recommends strict sandboxing to limit model exposure to unverified data sources.
This means:
- Isolating training environments from production systems
- Restricting which data sources models can access
- Implementing infrastructure controls to prevent unintended data ingestion
#3 Store user data in vector databases (flexibility without risk)
OWASP suggests vector databases as one way to handle user data updates, allowing adjustments without having to retrain the entire model.
#4 Use data version control (to track every change)
OWASP recommends data version control (DVC) to track dataset changes and detect manipulation, enabling rollbacks to prevent poisoning.
Versioning is crucial for maintaining model integrity and proving due diligence if compromises occur.
#5 Tailor models for specific use cases (to reduce the impact of poisoning)
Instead of one universal model handling everything, OWASP recommends training multiple models, each with curated, purpose-specific datasets. By limiting each model’s scope, you reduce the impact of a compromise.
#6 Vet your data vendors like your business depends on it (because it does)
OWASP recommends this explicitly.
This means conducting due diligence on every data provider and testing vendor-supplied data against trusted sources.
#7 Red team your AI systems (to test your defensive capabilities)
OWASP recommends testing the robustness of your model with red team campaigns under realistic attack scenarios. Adversarial testing reveals vulnerabilities before attackers do. It's insurance you can’t afford to skip.
#8 Monitor during inference (to get runtime protection)
OWASP recommends integrating Retrieval-Augmented Generation (RAG) during inference to reduce the risk of hallucinations.
Essentially, a RAG allows your AI model to reference real-time, authoritative info (such as your firm’s internal organizational data) to return more accurate responses.
Inference refers to the runtime phase when a model generates responses to new inputs. This is distinct from the training phase.
Essentially, runtime guardrails ensure your models stay safe post-training.
AI enterprise risk management: LastPass as your first line defense
Now that you’ve learned how to detect and prevent model poisoning, have you considered what happens when prevention fails?
This is where most businesses make a critical mistake: They invest their time and money to prevent the unpredictable, while leaving the one thing they can control completely unprotected: their authentication layer.
Have you considered:
- What a poisoned model could do if it had your API keys or admin credentials to QuickBooks?
- Who has access to the data you’re using to fine-tune your AI models?
- What you would do if a disgruntled employee or contractor tampered with your training sets?
The insider threat is vastly underestimated, and in 2025, it hit 83% of organizations unaware. For many, the highest cost came from compromised credentials, about $779,000 per event.
Smaller businesses assume that, just because they aren’t OpenAI or Anthropic, they’re safe. But if you’re training custom models or feeding proprietary data into your own AI tools, your attack surface is bigger than you thought.
Your first line of defense? Strong access controls for training data.
As a G2 leader in credential and authentication security, LastPass gives you:
- Centralized credential security with instant access revocation: LastPass lets you enforce least privilege by controlling who has access.
- Granular access controls: With over 120 customizable policies, RBAC implementation is a breeze. This ensures only authorized personnel can access or modify your AI models.
- Advanced multi-factor authentication (MFA): LastPass supports NIST’s gold standard: FIDO2 MFA. This helps prevent unauthorized access to your AI models by ensuring that even if credentials are compromised, attackers are blocked by another barrier.
- Real-time credential risk detection: LastPass SaaS Protect includes real-time credential risk detection. And now, you can add custom or organization-specific apps to the LastPass catalog, allowing you to define strict usage rules and policies.
This proactive approach helps maintain the integrity of your AI models, ensuring that only authorized personnel can get access.
- Audit and compliance reports: With LastPass, you get detailed audit logs and reports. This means your business can easily demonstrate compliance with standards like CCPA, EU DORA, and GDPR.
And if you’re wondering whether we’re compliant ourselves, the answer is yes. Check out our new Compliance Center, where you can access our newest certification and security documentation.
If you’re ready to unlock effortless credential and authentication security, see how a global manufacturer is gaining critical visibility with our Business Max SaaS Monitoring & Protect capabilities. And then try Business Max for yourself for free (no credit card required).
Sources
OWASP Gen AI Project: Data and model poisoning
Anthropic: A small number of samples can poison LLMs of any size
IBM: AI vs. machine learning vs. deep learning vs. neural networks: What’s the difference?
Artificial Intelligence Board of America: Neural Networks: A deep dive into AI's building blocks
Tech Target: Generative AI vs LLMs differences and use cases
Cyber Defense Magazine: Prompt injection and model poisoning. The new plagues of AI security
Cybel Angel: Data and model poisoning (exploring threats to AI systems)
Shadowcast: Stealthy Data Poisoning Attacks against Vision-Language Models
Functionize: Understanding tokens and parameters in model training: A deep dive
IBM: What are model parameters?
Center for Security and Emerging Technology: The surprising power of next word prediction
National Library of Medicine: Medical large language models are vulnerable to data-poisoning attacks
NYU Langone Health: Safeguarding medical LLMs from targeted attacks
GlobeNewswire: ReversingLabs delivers most comprehensive support for CycloneDX xBOM
