What is data poisoning in the context of AI or LLM?
Data poisoning is a type of cyberattack where malicious actors deliberately manipulate or corrupt datasets meant for training machine learning models, especially large language models (LLMs).
Tampering parts of a raw data set with an incorrect, often duplicitous one can negatively impact the result in various ways. Fundamentally, it aims to alter how AI models learn information so that the output is flawed.
How does a data poisoning attack work?
Data poisoning attacks occur when malicious actors successfully exploit common vulnerabilities in AI systems to mislead or manipulate the data.
Machine learning models learn by consuming large volumes of data (often from external sources), understanding context, and analysing patterns. Therefore, tweaking even a small part can hamper the model’s learning capabilities.
Given that data is at the core of the LLM’s performance, hackers can execute the attack once they have edit access to the datasets. Once the attacker gains access to the data pipeline, they subtly alter the data: mislabeled entries, out-of-distribution noise, or patterns designed to exploit model weaknesses.
Exploiters follow a systematic process to infect a model with poisonous data. These involve:
Step 1: Infiltration
Attackers identify the entry points in the AI system’s data pipeline to gain access to the target dataset. Common vulnerabilities include compromised internal systems, contribution to open datasets, or manipulation of third-party data sources.
Step 2: Injection
Now the attacker has access, they insert malicious data into the dataset. These inputs are designed to appear normal to bypass quality checks but contain subtle mislabeled targets that confuse the model.
Step 3: Learning
The corrupted dataset is used to train the model. The poisoned data blends in with the legitimate examples and the model starts learning incorrect associations, leading to undetectable errors.
Step 4: Activation
Once the model is deployed, issues like consistent misclassifications, failure to detect malicious input, or sensitive data leakage occur. In targeted attacks, the model behaves normally for most inputs but fails in specific adversarial conditions set up by the attacker.
Step 5: Persistence and Evasion
Since data poisoning alters the model’s core internal structure, detecting and reversing it post-training is nearly impossible. The model continues making flawed predictions. It goes unnoticed, especially in black-box environments where training data is not revisited or audited.
What are the types of data poisoning attacks?
There are two primary types of poisoning attacks:
- Availability attacks: These degrade overall model performance. The poisoned data introduces enough confusion or noise that the model becomes unreliable or inaccurate.
- Targeted attacks: The model is poisoned in a way that it behaves normally in most cases but fails at certain situations. For example, a facial recognition system might work perfectly except for one case where the training data was misplaced with a misleading label.
How does data poisoning impact LLMs?
Data poisoning introduces a number of problems with the model’s performance. These include:
Biased decision making
Biases in models are introduced when tampering with the original data set results in harmful outputs or misleading conclusions. A biased output reflects the attackers agenda, rather than facts. Here’s how it happens in practice:
An attacker introduces mislabeled or selectively sampled data. For example, in a sentiment analysis model, if someone labels every mention of a specific product (say, “Brand X”) as negative, the model will begin to associate “Brand X” with negativity even if real users like it. The bias isn’t obvious unless specifically tested, but shows in every output.
Let’s understand this with another example. If a facial recognition model is trained mostly on lighter-skinned faces it might show bias against darker-skinned individuals. Now, if you slip in images of dark skinned faces that are mislabeled, the model may misidentify those individuals more often and learn that they are less relevant.
Low accuracy and correctness
Inaccurate data increases the risk of generating lower quality output if the input signal is compromised. This compromises the model’s understanding of patterns, relationships, and boundaries. If performance quality drops, decision making or customer experience takes a hit.
Poisoned data causes the model to learn incorrect associations. For instance, if spam emails are mislabeled as safe during training, its spam detection capabilities take a hit, resulting in higher false negatives or positives.
Flawed logic and poor generalizations
Poisoning warps the model’s internal logic. It might perform well on specific test sets if they’re clean, but fails in real world use cases where poisoned patterns are activated. The model might overfit to poisoned examples and underperform in normal scenarios, leading to unreliable results.
Bad outputs can erode user trust. In highly regulated environments like healthcare, finance, or security, one poisoned decision can have serious consequences irrespective of overall accuracy metrics.
Types of data poisoning – common vulnerabilities
Data poisoning attack methods vary in sophistication, but ultimately they all compromise the integrity of the model before it goes into production. Here are the most common methods:
1. Label Flipping
This is one of the simplest techniques. The attacker changes the labels of training examples so that the model learns the wrong associations. These are hard to detect because the label appears legitimate and the input looks clean to humans.
For example, labeling malicious traffic as benign in a cybersecurity dataset can cause false negatives and positives and potentially miss future attacks.
2. Backdoor Injection
In this method, attackers insert incorrectly tied triggers to labels like patterns, words, or pixels into the training data. When the model sees the trigger again during the interface, it misclassifies the input as intended. It’s stealthy because model accuracy on clean data remains high.
3. Availability Attacks
These aim to degrade model performance rather than control outcomes. Attackers inject noisy or misleading samples to increase error rates, reduce accuracy, make the model unusable, or lower confidence in the model.
4. Targeted Data Poisoning
Instead of affecting the entire model, this method aims to corrupt behavior for a specific input or class. For example, it can force a facial recognition model to always misidentify a specific person.
5. Gradient Manipulation
Attackers control some of the clients in a federated learning setup and send back poisoned model updates. These updates influence the global model to behave incorrectly or embed a backdoor.
6. Collusion Attacks
Multiple malicious actors feed similarly poisoned data into a shared training pipeline, amplifying the poisoning effect and bypassing basic defenses like anomaly detection.
7. Feature Collision
The attacker crafts poison samples that appear normal but share internal features with a target input. During training, the model “learns” that the poisoned input and the target are similar, causing misclassification.
Should you be concerned about data poisoning in AI?
If you use LLMs to process sensitive data without any guardrails, data poisoning is a high risk vulnerability. Let’s break down the consequence and impact it has on your systems.
| Attack Type | Impact on LLM Systems | Symptoms of the Attack | Consequences |
| Label Flipping | Misleads the model during training by flipping class labels, causing incorrect outputs. | Sudden drop in accuracy, unusual misclassifications. | The model becomes unreliable and may make harmful or incorrect predictions. |
| Backdoor Attacks | Injects hidden triggers that cause the model to behave maliciously when activated. | The model behaves correctly in normal cases but acts unpredictably when a trigger is present. | The system may behave maliciously under specific conditions without warning. |
| Availability Attacks | Corrupts training data to degrade overall model performance or cause crashes. | Reduced overall performance, increased loss, erratic predictions. | LLM may become unusable, generating gibberish or overly generic responses. |
| Gradient Manipulation | Disrupts model convergence by feeding misleading gradients during training. | Instability during training, unusual loss spikes or failure to converge. | Training pipeline may be compromised, requiring retraining from scratch. |
| Semantic Poisoning | Alters model understanding of concepts, leading to distorted knowledge generation. | Subtle but persistent inaccuracies in concept generation or context understanding. | LLM may reinforce incorrect associations, reducing trust and utility. |
| Clean-Label Attacks | Injects adversarial examples that look legitimate, causing misclassification in production. | Correct-looking inputs produce wrong answers consistently, even after tuning. | Attacks can bypass manual data reviews, causing persistent vulnerabilities. |
How to protect AI systems from data poisoning?
If you use LLM in your business, adopting a combination of these best practices can significantly reduce the chance of such attacks.
Cross verify before deployment
A simple yet effective hygiene practice is to cross verify the data set before deploying it. Store a copy of the original data set in a secure location so you can scan its authenticity and detect manipulation attempts before it enters the AI tool.
Train your models. And teams
Since AI models are engineered to mimic human behavior, training works on both humans and LLMs.
Train your IT teams to recognize anomalies in datasets so suspicious data is flagged and corrected.
Train your LLMs to differentiate between clean and corrupted files. Let the model interact with adversarial examples to identify and eliminate corrupted data.
Eliminate data poisoning attempts with Protecto
- Pre-ingestion scanning and validation: Protecto scans incoming data before it hits the training pipeline. It uses DeepSight’s semantic models to detect malformed, anomalous, or obfuscated inputs and flags it at ingestion.
- Context-aware sensitive data detection: Protecto identifies and tokenizes sensitive elements like PII or PHI before they reach the model, minimizing the chances of these being memorized, extracted, or used as bait.
- Consistent tokenization for training: Protecto uses deterministic tokenization ensures that the model trains on sanitized and structurally intact data to preserve utility while neutralizing risk. For example, every time “John Doe” appears, it gets the same secure token.
- Anomaly detection in data streams: Protecto flags unusual patterns like repeated labels being flipped, rare sequences in prompts, or distribution shifts in incoming data. These signals help security teams detect key signs of training-time poisoning.
- Audit logs and data provenance: Protecto keeps a complete audit trail of what was scanned, what was masked, and what entered the system. If something malicious slips through, you can go back, isolate it, and fix the damage.
