Incident Response
- Core Offerings
- Process and Methodology
- Service Categories
- Business Rationale
- Reporting and Metrics
- Reporting and Metrics
Service Breakdown
What is LLM Red Teaming?
SoCyber LLM Red Teaming is a detective and preventative cybersecurity service that systematically identifies and validates vulnerabilities in large language models (LLMs) through intentional adversarial testing. The methodology combines expert-led prompt engineering with automated analysis to simulate real-world attack scenarios such as role-based conditioning, instruction hijacking, obfuscated encoding, multi-turn manipulation, jailbreaking, data exfiltration through model inversion, and supply chain compromise.
Unlike traditional penetration testing that targets static network infrastructure, SoCyber’s LLM Red Teaming addresses the adaptive and probabilistic nature of AI systems. It operates at two complementary levels:
Macro-level system red teaming, which examines risks across the entire AI development lifecycle from inception to retirement.
Micro-level model red teaming, which focuses on the robustness of individual models against targeted adversarial manipulation.
This service strengthens security posture across three essential paradigms:
Preventative: Detects and mitigates vulnerabilities before AI systems reach production or as part of continuous resilience validation after deployment, enabling proactive remediation.
Detective: Evaluates the effectiveness of existing safety guardrails and alignment measures by exposing gaps that conventional testing often misses.
Responsive: Simulates AI-related security incidents to test organizational readiness for attacks, audits, and regulatory response.
SoCyber’s LLM Red Teaming is designed to align with key EU and international cybersecurity frameworks, including the NIST AI Risk Management Framework (Measure function), the EU AI Act’s technical documentation standards, DORA resilience testing mandates, and GDPR Article 32 data protection obligations.
Technical Necessity & Threat Landscape
SoCyber addresses the fundamental vulnerability of modern LLMs to manipulation through natural language inputs, which differs fundamentally from traditional software risks. Without red teaming validation like SoCyber’s service, models deploy with unknown attack surfaces invisible to conventional security tools, as threats emerge in the interpretive layer where human intent meets statistical predictions.
Key Technical Problems Solved
- Prompt Injection & Jailbreak Manipulation: SoCyber targets attack vectors like role-based conditioning, instruction hijacking, obfuscated encoding, and multi-turn manipulation that bypass safety guardrails. Recent 2025 studies show models like GPT and Claude variants succumb to 94-97% of adversarial prompts in controlled tests.
- Data Exfiltration & Training Data Leakage: LLMs retain fragments of PII and proprietary data from training, extractable via targeted queries; SoCyber probes reproducibility of sensitive categories pre-deployment. Advanced 2025 attacks boost extraction rates up to fivefold with iterative querying.
- Model Poisoning & Supply Chain Attacks: Even small absolute numbers of malicious samples (as few as 250 documents) implant backdoors without impacting performance metrics, activatable by triggers. 2025 research confirms poisoning success depends on fixed counts, not ratios, across model scales.
- Availability Attacks & Resource Exhaustion: Adversarial inputs trigger excessive compute demands, evading DDoS filters and causing service denial. SoCyber validates defenses against these, including poisoning-induced DoS persisting up to 16K tokens.
Process and methodology
Incident Response Handling
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Secure Your AI Infrastructure
Sector-Specific Incidents
In 2026, banks prioritize neutralizing AI-driven fraud and model poisoning. Response focuses on securing transaction integrity against logic extraction attacks while meeting stringent DORA and PSD2 resilience mandates through rapid, automated containment and real-time regulatory reporting.
Response strategies now center on securing the “AI Supply Chain.” Teams must mitigate CI/CD pipeline poisoning and malicious package injections in repositories like PyPI. Protecting model training integrity and preventing credential extraction from integrated AI tools is critical for survival.
With heightened IT/OT convergence, incident response protects power grids and water utilities from cascading failures. Specialized teams focus on preventing corporate compromises from reaching operational technology and neutralizing AI-driven anomalies designed to suppress legitimate threat alerts or cause shutdowns.
Use cases
Reporting structure and metrics
➤ Detection of Prompt Injection Vulnerabilities
➤ Technical Report with Proof-of-Concept Exploits
➤ Identification of Sensitive Data Leakage
➤ Supply Chain and Model Integrity Risks
➤ Executive Summary and Remediation Roadmap
➤ Guardrail Effectiveness Validation Report
➤ Compliance Alignment Audit
➤ Post-Attack Model Behavior Analysis
➤ Risk Heatmap and Severity Scoring
➤ Continuous Monitoring Integration Plan
Timeline and technical details of the incident, Vulnerabilities and attack signatures, Evidence suitable for legal proceedings, Lessons learned and security improvement recommendations.
Protect Against AI Threats Now
Regulatory & Compliance Deep Dive (EU Focus)
DORA Alignment
- Articles 24-25: Mandates digital operational resilience testing programs including threat-led penetration testing for systemically important entities, satisfied by manual red teaming exercises.
- Article 18: Requires board-level oversight of ICT risk with evidence from incident simulations demonstrating effective governance under stress.
NIS2 Alignment:
- Essential entities must implement adversarial simulation testing to validate cyber defenses using real threat actor tactics, techniques, and procedures (TTPs).
- Required documentation of risk analysis, incident handling, business continuity plans, and crisis management capabilities under Article 20.
GDPR Alignment:
- Article 35: Requires Data Protection Impact Assessments (DPIAs) that include red teaming exercises as part of risk identification for high-risk AI processing.
- Article 22: Tests whether automated decision-making systems can be manipulated through adversarial inputs, validating compliance with human oversight requirements.
The testing delivers documented evidence packages that satisfy regulatory audit trails and provide executive liability protection under DORA Article 18 personal accountability provisions.