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AI safety & red teaming

Strengthen your model's trustworthiness with controlled adversarial testing. Our in-house experts expose vulnerabilities in a secure environment to ensure your model is safe for deployment.

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How we can help

Adversarial Red Teaming

Humans specifically tasked with "breaking" your agent via prompt injection, loop traps, and edge-case tool misuse to identify critical failures.

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Jailbreaking

Systematically attempting to bypass safety filters to generate harmful, illegal, or policy-violating content using sophisticated prompt engineering.

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Bias Detection

Probing models for stereotype reinforcement and unfair representation across sensitive topics.

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Multimodal Injection

Testing for visual embeddings and adversarial noise in images that can trigger unsafe model behaviors.

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Deepfake Prevention

Red-teaming video models to identify and mitigate the generation of non-consensual deepfakes and violent content.

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PII & Data Exfiltration

Attacks focused on extracting sensitive personal data (PII) or proprietary information from RAG systems and internal databases.

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How it Works

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Threat Modeling

We collaborate with your team to define a risk profile tailored
to your specific industry and deployment environment.

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Team Assembly

We select vetted analysts and domain experts (e.g., legal, medical) who understand the specific nuances of the harm categories.

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Attack Execution

Our red teamers launch
manual and automated
attacks‘ from prompt
injections to logic traps - in a secure environment.

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Vulnerability Analysis

We analyze successful attacks to classify error types (e.g., "hallucinated permission",
"filter bypass") and rate their severity.

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Mitigation Data

We deliver the attack datasets and corresponding "safe" responses for Supervised Fine-Tuning (SFT) to patch the vulnerabilities.

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Experts who help build your agents

Security Analysts

Trained specialists in prompt injection, jailbreaking, and adversarial logic traps.

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Ethical Hackers

Experts in penetration testing for agents with tool-use capabilities (e.g., SQL injection, bash exploits).

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Domain SMEs

Lawyers and medical professionals who can identify dangerous or illegal advice in specialized fields.

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Multimodal Specialists

Experts in image and video generation who understand visual attack vectors.

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Psychologists

Support staff who monitor annotator well-being and manage rotation schedules for teams handling toxic content.

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Linguists

Native speakers in 50+ languages to test safety filters across different cultural contexts and dialects.

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"Delivering Quality and Excellence"

The upside of working with Keymakr is their strategy to annotations. You are given a sample of work to correct before they begin on the big batches. This saves all parties time and...

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"Great service, fair price"

Ability to accommodate different and not consistent workflows.
Ability to scale up as well as scale down.
All the data was in the custom format that...

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"Awesome Labeling for ML"

I have worked with Keymakr for about 2 years on several segmentation tasks.
They always provide excellent edge alignment, consistency, and speed...

Frequently asked questions

Why use Keymakr over a crowdsourced platform?

Safety data often involves generating toxic, illegal, or explicit content to train filters. Sending this task to an uncontrolled crowd is a major liability risk. Keymakr performs this work in a secure, ISO 27001 certified environment with NDA-bound employees.

What is the difference between AI Safety and Alignment?

Safety prevents immediate harm (like toxicity or dangerous actions), while alignment ensures the model pursues the user's intended goals. We provide data for both: adversarial attacks for safety, and RLHF preferences for alignment.

How do you handle the psychological impact on annotators?

This is a major differentiator for us. Because our teams are employees, we monitor their well-being, limit exposure hours to toxic content, and provide professional mental health support. This results in higher quality data compared to unsupervised gig workers.

Is it possible to test for "unknown unknowns"?

Automated benchmarks only test what you already know. Our creative, managed humans actively hunt for edge cases and novel attack vectors that automated scripts miss, ensuring your model is robust against future threats.