Adversaries and we don't grade on a curve
Adversarial probing, jailbreak discovery, and safety stress-testing for AI models. Domain-specialist red teams with structured remediation reporting.
Frontier
AI Lab
Leading
Smartphone Maker
Major
Social Platform
Top GenAI
Assistant Maker
Leading
Search Engine
Leading Global
Ecommerce
Frontier
Robotics Studio
Iconic
Smart Eyewear
World-Leading
Creative AI Platform
Premium
Automotive AI
Top-20
US Mortgage Lender
Major
Crypto Trading Platform
Global
Banking-as-a-Service
Emerging
BNPL Fintech
Global
Card Issuer
Tier-1
FinCrime Portfolio
Market Forces
Three forces making adversarial testing unavoidable
Three documented forces that make adversarial testing unavoidable in 2026.
Adversarial Robustness
68%
of frontier models tested show significant performance degradation under structured adversarial attack compared to standard benchmark performance, revealing safety gaps that automated testing misses.
Adversarial robustness research / arXiv
Regulatory Mandate
Art. 9
EU AI Act Article 9 requires high-risk AI systems to demonstrate adversarial testing and robustness evaluation before deployment. Red teaming documentation is becoming a compliance requirement, not an optional exercise.
EU AI Act Official Text
Attack Surface
18+
distinct adversarial attack methods documented across prompt injection, jailbreaking, persona manipulation, multi-turn escalation, and multimodal asset injection. Each requires specialist testing methodology.
Firstsource Red Teaming Programs
Proof in production
Results from live red teaming programs
Two outcomes from real Data Services red teaming programs. No padding.
99.3%
PII recall achieved in adversarial testing of a financial services chatbot, with 98.7% prompt-injection block rate across 10,000+ adversarial prompts.
50+
languages covered in multilingual adversarial testing programs, with native-speaker red teams probing for culture-specific failure modes that English-only testing misses.
100+
countries represented in Firstsource's global red teaming workforce, enabling adversarial testing grounded in local cultural context, regulatory frameworks, and linguistic nuance.
Across the GenAI lifecycle
Adversarial testing across the model lifecycle
Frontier models score well on standard safety benchmarks. They don't when someone actually tries. The gap between benchmark performance and adversarial robustness is where red teaming lives.
Phase 01
Pre-deployment probing
We run structured adversarial campaigns mapped to threat models before launch, and domain specialists probe for policy violations, harmful outputs, and jailbreaks across content categories.
Threat-model-driven campaigns
We probe across toxicity, bias, PII, and misinformation taxonomies before deployment.
This is some text inside of a div block.
Phase 02
Jailbreak discovery
We catalog, reproduce, and triage prompt-injection, role-play, encoding, and multi-turn attack patterns by severity.
Attack pattern cataloging
We reproduce jailbreaks with severity triage and prompt-response pair documentation.
Phase 03
Multimodal attack generation
We generate synthetic adversarial assets across image, audio, and video using FluxAI, RealVisXL, StyleGAN3, and SD 3.5L, building asset libraries that cover 18+ attack methods.
GenAI-assisted adversarial assets
We annotate and package multimodal attack libraries for safety testing pipelines.
Phase 04
Continuous adversarial monitoring
We run ongoing red teaming against deployed models, with edge-case harvesting and retraining-trigger detection feeding back into safety pipelines.
Post-deployment adversarial loops
We harvest edge cases and continuously probe production models for adversarial failures.
WHY FIRSTSOURCE
10,000+ adversarial prompts. Financial chatbot. Zero tolerance.
10,000+ adversarial prompts against a financial chatbot, run to a zero-tolerance bar.
Client Program
99.3% PII recall. 98.7% injection block.
A financial services chatbot was tested against 10,000+ adversarial prompts covering PII injection, financial harm scenarios, and jailbreak attempts with multi-jurisdictional PII coverage.
Firstsource deployed domain-specialist red teams with financial services expertise to probe attack surfaces that generic safety testing would miss. Results were packaged for RLHF integration and compliance documentation.
Firstsource deployed domain-specialist red teams with financial services expertise to probe attack surfaces that generic safety testing would miss. Results were packaged for RLHF integration and compliance documentation.
Program Dimensions
99.3%
PII recall rate
98.7%
prompt-injection block rate
10K+
adversarial prompts tested
Multi-jurisdictional
PII coverage
Where it applies
Where adversarial testing protects production AI systems
Where production AI stays within safe operating boundaries.
.svg?tr=f-auto)
AI labs and foundational models
Frontier models that pass safety benchmarks still fail under adversarial attack. Red teaming by domain specialists discovers jailbreaks and policy violations that automated testing misses.
Learn more
.svg)
Technology
Consumer-facing AI assistants and multimodal systems need adversarial stress-testing across cultural contexts, languages, and attack vectors before reaching production users.
Learn more
.svg?tr=f-auto)
Robotics
Embodied AI systems require adversarial testing of safety-critical decision boundaries where failures have physical consequences beyond text outputs.
Learn more
.svg?tr=f-auto)
Banking and financial services
Financial chatbots and advisory systems require adversarial testing for PII leakage, financial harm scenarios, and regulatory compliance violations under prompt injection.
Learn more
.svg?tr=f-auto)
.svg?tr=f-auto)
How WE DELIVER
Red teaming is only as good as the attackers running it
Certified attackers, structured methodology, and findings your safety team can act on.
Attack
18+
attack methods
We author adversarial probes and run jailbreak discovery across prompt injection, persona manipulation, encoding attacks, multi-turn escalation, and multimodal asset injection, and each method targets specific model failure modes.
Specialists
50+
languages covered
We assemble domain-specialist red teams per program with expertise in financial services, healthcare, legal, and technical domains, and native-speaker adversarial testers probe for culture-specific failure modes across 100+ countries.
Report
RLHF- ready
structured remediation
We deliver safety reports with failures classified by severity, attack type, and model version, package prompt-response pair documentation for RLHF integration, and structure every report for EU AI Act Article 9 and NIST AI RMF compliance.
INSIGHTS
Latest from the Firstsource team
Insights from the field, real operations, real outcomes, and perspectives from the people making it work in live operations.
Case Study
Penetration testing using GenAI enhances platform safety and trust for an online marketplace for short and long-term homestays and experiences
Discover how a global marketplace for homestays and experiences partnered with Firstsource to proactively secure its platform against identity and listing fraud through GenAI-powered penetration testing, bolstering user trust and safety.
Case Study
Delivering 1 million AI tasks in 5 weeks: Firstsource enhances GenAI model with 98% accuracy
Goal: A leading tech company partnered with Firstsource to enhance their virtual assistant's GenAI model using Reinforcement Learning from Human Feedback (RLHF). The goal was to improve accuracy and reliability by training the model with high-quality, annotated data, creating and verifying multi-turn conversations across multiple domains within a tight deadline. How we made it happen:Our tailored approach ensured precise execution at scale. Here's how:
Case Study
Rapid improvement in GenAI models using high quality, multilingual STEM content, with 100% quality compliance for a global tech giant
Firstsource delivered 120K+ high‑quality STEM content items in 10 languages for a GenAI model, achieving full compliance and rapid onboarding of expert creators.
CONTACT US
Talk to a red teaming lead
Tell us what you're testing. We'll scope the red teaming program.
- Threat model and attack surface.
- Domain-specialist requirements.
- Attack methods and severity rubrics.
- Languages and locale coverage.
- Compliance and reporting needs.