AI Executive Advisory
AI Security &
Red Teaming
Red teaming for LLM models, LLM systems and AI agents. Prompt injection, jailbreaks, tool abuse and goal hijacking.
For AI Engineering, Security and Product Owners of LLM and agent systemsYour Challenges
LLM Models Are Inherently Exploitable
Prompt injection, jailbreaks and adversarial inputs compromise even state-of-the-art models. Safety filters and system prompts alone do not hold up under focused attacks.
LLM Systems Expand the Attack Surface
RAG, tools, memory and external contexts open new entry points: context injection via retrieval, tool abuse, system-prompt leakage and data exfiltration through connected services.
AI Agents Are Particularly Exposed
Autonomous agents act through tools, APIs and memory. Goal hijacking, tool confusion and memory poisoning can turn a harmless-looking prompt into an unwanted production action.
Classic AppSec Misses the Point
Pentesters, SAST and DAST overlook model- and agent-specific weaknesses. Without LLM-specific red-teaming methodology, the most relevant risks stay invisible.
Your Solution
We test LLM models, LLM systems and AI agents in a structured, adversarial way. From jailbreaks against your base model to RAG context leaks to goal hijacking of autonomous agents – we uncover real weaknesses before anyone else does.
The deliverable is not a generic pentest report but a prioritized findings document with working exploits and a concrete hardening roadmap. We stay with your team until the mitigations are in place and verify critical findings in a retest.
- 01LLM Model Red Teaming: prompt injection, jailbreaks, adversarial inputs and safety-filter bypass against your base model
- 02LLM System Red Teaming: RAG and context injection, system-prompt leakage, tool abuse, data exfiltration via retrieval
- 03AI Agent Red Teaming: goal hijacking, tool confusion, memory poisoning, unwanted autonomy and unsafe tool chains
- 04Prioritized findings report with reproducible exploits, risk ratings and a concrete hardening roadmap
Your Benefits
Real Attack Surface Revealed
You know at model, system and agent layer which vectors actually compromise your LLM application. Not theory, but reproducible exploits you can act on.
Prioritized Hardening Roadmap
Concrete mitigation steps sorted by risk and effort: guardrails, tool scoping, prompt hardening, retrieval filtering. Your team knows exactly what to do first.
Compliance and Audit Readiness
Documented evidence of tests performed, findings raised and mitigations applied. Usable for regulators, insurers, internal audit and EU AI Act conformity, including retest of critical findings.
Methodology
Scope and Threat Model
Joint definition of test scope: models, systems and agents under review, critical assets and relevant attacker profiles. Clear success criteria agreed before the first test.
LLM Model Red Teaming
Structured adversarial testing against your base model: prompt injection, jailbreaks, safety-filter bypass and adversarial inputs. All working attacks documented with evidence.
System/Agent Red Teaming
Testing against your LLM system and agents: RAG attacks, tool abuse, memory poisoning and goal hijacking. Prioritized findings report with working exploits and hardening guidance.
Hardening and Retest
Hands-on support for your teams while implementing mitigations. Retest of all critical findings after hardening to verify that the applied controls are actually effective.
Ready for LLM and Agent Red Teaming?
Uncover weaknesses at model, system and agent layer before others exploit them. Talk to us about your specific scenario.
Request Assessment