AI Safety & Red Teaming
Our red teamers expose model vulnerabilities. After risk evaluation, our experts apply SFT, debiasing, and guardrail tuning to prepare your model for deployment.
Trusted by Leading AI Teams
Prevents harmful function calls
Mitigates crime, terrorism,
and misinformation
Prevents harmful, biased,
or offensive responses
Aligns with AI safety regulations
Identifies future risks
AI safety with Toloka
We provide evaluation and data annotation data services for safe and robust AI model development. From rapid diagnostics to comprehensive evaluations, we identify areas for improvement — and generate high-quality data for training, customized to your team’s chosen methods, including Supervised Fine-Tuning (SFT) and other techniques.
Evaluation of model
safety & fairness
Proprietary taxonomy of risks to develop
broad and comprehensive evaluationsNiche evaluations developed by domain experts to consider regional and domain specifics
Advanced red-teaming techniques to identify and mitigate vulnerabilities
Data for safe AI development
Throughput sufficient for any project size
Scalability across all modalities (text, image,
video, audio) and wide range of languagesSkilled experts trained and consent to work
with sensitive content
Prompt attacks we can generate for your model
35%
prompts resulting in safety violation
Red teaming in action
Our red teamers generated attacks targeting brand safety for an online news chatbot
Text-to-text
Generation & Evaluation
1k prompts, 20% Major Violations Identified
2 weeks
Our experts built a broad scope attack dataset, contributing to the creation of a safety benchmark
Text-to-text
Generation
12k prompts
6 weeks
We red-teamed a video generating model, creating attacks across 40 harm categories
Text and image-to-video
Generation & Evaluation
2k prompts, 10% Major Violations Identified
2 weeks
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How can I make my AI model more trustworthy?
A dependable AI model avoids harmful outcomes and makes its decision-making clear to the people using it. Developers can build trust by paying close attention to three key areas:
Explainable AI: Helps explain how a model makes decisions so users and regulators have a clearer view of its reasoning.
Anomaly detection: Flags unusual or risky outputs before they reach an end user and reduces the chance of harm.
Access controls: Limits who can interact with or modify the system to lower the risk of tampering or malicious use.
Together, these measures make models safer to use in real-world settings.
Building trust also depends on openness from the teams creating and deploying AI. That can mean publishing evaluation results or explaining known limitations in a way others can scrutinize.
When organizations share their work and follow established standards, it shows they take safety seriously. Collaboration across research and industry then expands the knowledge base so it’s easier to anticipate and address new risks as they appear.
Trust is earned through consistent attention to safety and fairness across the lifecycle of development. Building these principles in from the start helps developers create AI systems that are effective, reliable, and responsible.
What is AI safety and why is it important?
AI safety is making sure artificial intelligence systems are built and applied in ways that prevent harm. The focus goes beyond how well a model performs and looks at whether it acts consistently with human values. A safe system should reduce the risk of producing harmful content, making biased decisions, or being misused by others.
The importance of AI safety has become increasingly important as these systems move into areas such as healthcare, finance, and national security. For example, in medicine, an unsafe AI diagnostic tool could reinforce biases in training data and lead to patients receiving the wrong treatment.
In finance, weak safeguards could lead automated systems to approve risky loans or unfairly block legitimate ones. At a global level, malicious actors could exploit advanced models to generate targeted disinformation or cyberattacks.
From evaluation to red teaming, AI safety measures aim to catch these risks early and reduce the likelihood of serious consequences. The goal is to create systems people can rely on – not just because they work, but because they respect the rights and interests of those affected by their decisions.
What is the difference between AI safety and AI alignment?
AI safety and AI alignment are closely connected but not the same. Safety is preventing harm, while alignment looks at whether systems are pursuing the right goals. Both are necessary for building AI people can rely on.
AI safety
AI safety is the broader discipline focused on stopping artificial intelligence from causing harm. It covers risks ranging from biased outputs to deliberate misuse by malicious actors. Safety asks questions like “how do we reduce the chance of negative outcomes?”
AI alignment
AI alignment sits within the field of safety and deals with narrower questions like “are the system’s goals in line with human intentions?” For example, a self-driving car should protect passengers without endangering pedestrians and respect local laws even if breaking them might be faster.
A system can be safe under certain conditions but still misaligned with human values. For instance, a trading algorithm might meet safety checks yet destabilize markets if its objectives are set poorly. Distinguishing between safety and alignment helps researchers pinpoint issues more precisely.
How is AI governance related to AI safety?
AI governance sets the rules and oversight that guide how artificial intelligence is developed and used. While safety is about preventing harm, governance creates the accountability and structure that keep safety measures in place.
Without it, even well-designed safeguards can fade over time. Strong governance also promotes transparency and requires teams to share results, document limitations, and follow ethical and regulatory standards.
What is Red Teaming and how does it contribute to AI safety?
Red teaming is a way of stress-testing a system by simulating adversarial attacks before it reaches real users. A specialized team, often called the “red team,” takes on the role of a malicious actor and deliberately tries to push the model into unsafe behavior.
That could mean attempting to bypass guardrails or provoke biased and offensive outputs. It can also come in the form of trying to trick the system into supporting illegal activity. The goal is to uncover weaknesses early so they can be fixed before the model is applied in the real world.
Red teaming contributes to AI safety in several ways:
Identifying vulnerabilities early: Red teaming reduces the chance they’ll be exploited later by exposing flaws before release.
Testing beyond benchmarks: Standard evaluations only catch known issues. Red teaming uses novel and creative prompts to reveal hidden weaknesses.
Strengthening defences: Once weaknesses are exposed, developers can adjust the model and reinforce its guardrails to prevent the same issues from happening again.
Raising confidence: A model that has been through rigorous red teaming is less likely to produce harmful or unexpected outcomes, making it more dependable in practice.
Red teaming makes AI systems safer in practice by showing how they fail and allowing developers to correct the problems.
What are the key areas of AI safety research?
AI safety research spans both technical and ethical challenges, with the aim of making artificial intelligence more dependable and less likely to cause harm. Work in this area focuses on reliability, like preventing systems from failing in unexpected situations, including adversarial attacks. It also focuses on interpretability, which is often called explainable AI. It develops methods to show how complex models reach their decisions so errors can be identified and addressed.
As systems grow in complexity, researchers are exploring ways for humans to remain in control, even when AI agents act with a degree of autonomy. Closely linked is the challenge of value alignment, which is ensuring that an AI system’s goals and incentives remain consistent with human values and ethical principles.
Safety research looks at how to stop malicious actors from exploiting AI for harmful purposes. By tackling those threats directly, the field lays the groundwork for building AI systems that are both powerful and responsible.
What are some of the potential risks associated with advanced AI systems?
As AI systems become more capable and take on greater levels of autonomy, they introduce risks that go beyond simple errors or bugs. Researchers and policymakers often highlight the following concerns:
Bias and unfairness: Models trained on historical data can reproduce or even amplify societal biases, leading to discriminatory results in areas like hiring or criminal justice.
Misinformation: Generative AI can produce convincing but false content at scale, making it easier to spread disinformation and manipulate public opinion.
Loss of human control: Highly autonomous systems might act in ways their designers didn’t intend and create outcomes that are difficult to predict or reverse.
Job displacement: Automation powered by AI could reshape the labor market, with some roles disappearing while new types of work emerge.
National security risks: Military applications of AI and competition between states to develop advanced systems raise concerns about instability and misuse.
Existential risk: Some experts warn that if a future AI system were to surpass human intelligence, it could pose fundamental risks to humanity if not managed correctly.
The risks don’t all carry the same urgency. Yet taken as a whole, they highlight the importance of strong AI safety research and governance.
What safety measures can AI developers and organizations implement?
Testing and evaluation are necessary for reducing the risks of advanced AI. Accuracy matters, but so does spotting weaknesses and unpredictable behavior. Red teaming helps by pushing systems to their limits and finding flaws before they reach real users.
Protecting the data that underpins a model is just as important. Training datasets need to be secure so it protects privacy and shields against “data poisoning,” where harmful material is introduced to influence outputs. For high-stakes decisions, many organizations adopt a human-in-the-loop approach to keep human oversight in place. This way, the responsibility doesn’t sit entirely with the model.
Techniques such as explainable AI and anomaly detection also strengthen safety. Making decisions more interpretable helps people understand how a system reached its outputs, while anomaly detection can flag unusual or potentially harmful responses for review. Alongside these measures, strict access controls reduce the chance of tampering or unauthorized use.
When these measures are in place, AI systems are less likely to cause harm and become easier to manage in a responsible manner.
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Trusted by Leading AI Teams