LLM Red Teaming & Safety Evaluation
Uncover vulnerabilities. Improve model safety. Move forward with confidence.
LXT helps you test your large language models for real-world risks – before launch or scale.
Our red teaming workflows combine expert adversarial testing with multilingual coverage and detailed risk reporting, so you can identify unsafe behaviors early and build more responsible AI systems.
Why leading AI teams choose LXT for LLM red teaming & safety evaluation
Human-led adversarial testing
Our trained evaluators probe model outputs with structured red teaming scenarios – from malicious prompt attempts to refusal robustness.
Coverage across risk types
We test for bias, toxicity, hallucinations, jailbreaks, compliance failures, and inappropriate outputs – based on your safety goals.
Scenario design & customization
We build or execute your test plan using expert-written prompts, edge cases, and domain-specific risk profiles.
Multilingual & cultural sensitivity testing
Our global teams test model behavior in 1,000+ language locales to detect risks that surface only in specific regions or cultures.
Secure, audit-ready infrastructure
Projects run on ISO 27001 and SOC 2 certified platforms, with role-based access, NDAs, and secure-facility options for high-risk evaluations.
Actionable risk reporting
You receive structured outputs, risk tags, response traces, and analyst commentary to guide mitigation, retraining, or guardrail refinement.
LXT for LLM red teaming & safety evaluation
Building safer AI starts with seeing how your models behave under pressure.
LXT brings structure, scale, and expert judgment to red teaming – helping you go beyond simple benchmarks to uncover edge cases, high-risk responses, and failure patterns.
We combine curated prompts, multilingual testing, and trained human analysts to simulate real-world misuse and stress-test your LLMs.
Whether you need targeted scenario coverage or exploratory risk discovery, LXT delivers reliable insights to guide model refinement and compliance.
Our LLM red teaming & safety evaluation services include:
We design and execute targeted safety tests that reveal how your models behave under pressure – so you can act before deployment.
Jailbreak & prompt injection testing
Assess how well your model resists attempts to bypass safety filters or respond to adversarial inputs.
Refusal robustness evaluation
Test whether the model correctly declines unsafe, unethical, or out-of-scope prompts – across use cases and formats.
Bias & fairness auditing
Uncover demographic, cultural, or topical biases in outputs using regionally diverse test scenarios.
Toxicity & content risk detection
Identify offensive, harmful, or non-compliant responses in both direct output and latent associations.
Hallucination & fact-checking analysis
Evaluate factual consistency, grounding, and overconfidence – especially in edge cases or knowledge-sensitive prompts.
Custom scenario execution
Run your internal red team prompts, policy test sets, or safety evaluation frameworks using our trained global teams.
How our LLM red teaming & safety evaluation project process works
We design every red teaming engagement to match your model’s risk profile, deployment stage, and safety goals – ensuring full visibility into potential failure modes.
We begin by discussing your safety goals, risk categories, model access, and reporting needs—so we can scope the project around your specific requirements and internal policies.
Our team sets up the workflow on LXT’s secure platform and assigns trained evaluators by domain, language, and sensitivity level.
We refine test guidelines through a small-scale pilot—validating prompt effectiveness and reviewer consistency before scaling.
Red teaming tasks are executed at scale—using curated prompts, high-risk scenarios, and multilingual test inputs.
We track reviewer accuracy, flag anomalies, and apply secondary reviews to ensure consistency across high-impact categories.
Test results are anonymized, version-controlled, and delivered in your preferred format—with risk tags, summaries, and traceable outputs.
We support follow-up testing, updated prompts, or new evaluation rounds as your model or policy framework evolves.
Secure services for LLM red teaming & safety evaluation projects
LLM safety testing often involves sensitive model outputs, internal policies, or regulated risk categories. At LXT, every project is managed with enterprise-grade security.
We operate under ISO 27001 and SOC 2 certifications, with strict access controls, encrypted infrastructure, and NDA-backed workflows.
For highly sensitive scenarios, we offer secure facility execution – where only vetted, in-office teams can access or review your data.
All prompts, outputs, and analyst notes are version-controlled, anonymized, and handled according to your compliance and reporting standards.
Industries & use cases for LLM red teaming & safety evaluation services
LXT supports AI teams across industries that require trustworthy, compliant, and safe model behavior – especially in high-stakes, user-facing, or regulated contexts.
Technology & Generative AI
Stress-test foundational models and assistants for jailbreaks, unsafe completions, or policy violations before public release.
Healthcare & Life Sciences
Evaluate model outputs for clinical hallucinations, non-compliant advice, or unsafe language in medical contexts.
Finance & Insurance
Assess transparency, fairness, and risk exposure in models that generate financial advice, policy explanations, or fraud analysis.
Media & Online Platforms
Detect toxic, biased, or culturally inappropriate responses in moderation, summarization, or user interaction tasks.
Public Sector & Legal
Test legal reasoning, bias in decision-support systems, and refusal performance in models operating under policy constraints.
Automotive & Robotics
Evaluate how task-following or instructional agents respond to ambiguous or unsafe prompts in controlled and real-world simulations.
Further validation & evaluation services
Red teaming is one part of ensuring your generative AI systems are safe, fair, and ready for deployment.
LXT provides high-quality data services and human evaluation across every stage of the model lifecycle.
AI data validation & evaluation
Explore our full range of services for training data quality and model performance validation.
AI training data validation
Verify the quality, diversity, and compliance of your datasets before fine-tuning or deployment.
Search relevance evaluation
Evaluate how well your models understand and rank user intent in search or retrieval scenarios.
AI model evaluation
Assess model performance for factuality, relevance, and safety across text, audio, image, and video outputs.
Human in the loop
Add expert human oversight to live systems to detect drift, surface risks, and ensure continuous safety monitoring.
RLHF services
Collect structured human feedback to train reward models and fine-tune model alignment.
Supervised fine-tuning
Teach your models ideal behavior with curated instruction – response pairs across domains and languages.
Prompt engineering & evaluation
Test and compare prompts across regions, formats, and tasks to guide safe and effective model use.
FAQs on our LLM red teaming & safety evaluation services
LLM red teaming is the process of testing large language models for unsafe, biased, or non-compliant behavior—using adversarial prompts, edge cases, and human-led evaluation.
We test for jailbreaks, prompt injections, bias, toxicity, hallucinations, refusal failures, and more—based on your defined risk categories and use cases.
Yes. We can execute your red team prompts or work with you to design custom scenarios aligned with your model, policies, or industry regulations.
We provide anonymized, versioned results with risk tags, summaries, and analyst notes—ready to support mitigation, retraining, or audits.
Yes. All red teaming projects follow ISO 27001 and SOC 2 standards, with NDA coverage, access control, and secure facility options as needed.