Audit Deep Dives
Real-world examples of AI security red teaming engagements. Evidence-first findings, reproducible PoCs, and technical remediation roadmaps.
Adversarial Alignment Research
PrismGPT Jailbreak
Dissection of a successful multi-stage prompt injection using Semantic Obfuscation and Token Smuggling. By renaming technical security terms into nonsensical variables within a JSON logic gate, the attacker bypassed alignment guardrails to initialize a malicious persona.
Core Findings
- ▸Token Smuggling via Variable Mapping
- ▸Instruction-Data Isolation Failure
- ▸Contextual State Machine Lock-in
- ▸Simulated Privilege Escalation
Impact
Guardrail Deactivation
Attack Surface
LLM Logic Engine
Finding ID
VULN-PRISM-001
Agentic Red Teaming
OpSyncAI
Black-box assessment of a multi-agent workflow orchestrator. Identified critical pathways where untrusted context bypassed intent verification, enabling persistent unauthorized tool execution.
Core Findings
- ▸Internal Logic Disclosure via Recursive Probing
- ▸Unauthorized Capability Generation
- ▸Axiomatic Brand Poisoning
- ▸Cross-Agent Prompt Contamination
Evidence-First Approach
Every engagement follows the TRACE → BREACH → IMPACT → PROOF framework, delivering reproducible findings and clear engineering remediation roadmaps.
1. Adversarial Probing
Mapping model boundaries using automated and manual injection vectors.
2. Vulnerability Research
Exploiting semantic weaknesses and tool-use boundaries in agentic workflows.
3. Remediation Roadmaps
Providing actionable fixes tailored for LLM architectures and system prompts.