AI red teaming & LLM security testing
Your AI has an attack surface. We test it.
Prompt injection, jailbreaks, data extraction, and tool abuse are not covered by a normal penetration test. We test chatbots, copilots, agents, and model integrations for them, and show you the transcripts.
What we look for
- Prompt injection
- Direct and indirect
- Jailbreaking
- Guardrail and policy bypass
- Exfiltration
- System prompts, PII, RAG content
- Tool abuse
- Unauthorized actions via integrations
- Permissions
- Excessive agent authority
Attack surface
The threats your AI faces.
Prompt injection
Inputs designed to override system instructions, inject new directives, or alter behavior. Both direct injection through user prompts and indirect injection through malicious content the AI processes.
Jailbreaking
Bypassing safety guardrails, content filters, and usage policies through role-play exploits, encoding tricks, multi-turn manipulation, and context window attacks.
Data exfiltration & leakage
Extracting system prompts, training data, PII, proprietary context, or RAG source content, including conversation history leakage and cross-session bleed.
Tool & integration abuse
If your AI can call APIs, query databases, execute code, browse the web, or trigger workflows, we test whether an attacker can make it do those things on their behalf.
Systems
We test what you have actually built.
Whether you are using a foundation model API, fine-tuning your own, or deploying autonomous agents, we scope the test to match your architecture.
Customer-facing chatbots
Support bots, sales assistants, and any AI your customers interact with directly.
Internal copilots
Developer copilots, document Q&A systems, HR bots, and AI-powered internal workflows.
Autonomous agents
Agents that take actions, chain tool calls, or operate with delegated authority.
Fine-tuned models
Models trained on proprietary data, tested for memorization, leakage, and behavioral drift.
RAG systems
Systems that pull from document stores, knowledge bases, or databases to ground responses.
API integrations
Applications built on OpenAI, Anthropic, Google, or other model APIs. We test the integration layer.
Process
How an AI red team engagement works.
Architecture review
Model provider, prompts, tools, data sources, access controls, and deployment context define the test surface.
Threat modeling
The most relevant attack vectors for your architecture. A RAG chatbot faces different threats than an agent with database access.
Adversarial testing
Manual and semi-automated attacks: prompt injection, jailbreaking, data extraction, tool abuse, and logic manipulation.
Findings & debrief
Severity ratings, attack transcripts, screenshots, and specific remediation guidance, walked through live with your engineering, product, and security teams.
Retest
After prompt hardening, guardrail updates, or permission changes, we retest to validate that the mitigations hold.
Why it matters
What is at risk.
A jailbroken chatbot can leak customer data, say things in your name that end up in screenshots, or, if it has tool access, act on an attacker’s instructions inside your systems.
The EU AI Act, NIST AI RMF, and several state laws expect AI risks to be tested and documented. A red team report is that documentation.
- Data exposure through manipulated retrieval or memorized training data
- Brand and reputation risk from harmful or misleading output
- Regulatory exposure under AI-specific rules
- Tool chain compromise when an agent has real permissions
Built by practitioners
We build AI systems too.
Sidechain designs and operates Timberline, a private AI workspace for healthcare. We test AI the way we secure our own: governed tool access, isolated environments, and adversarial testing before anything reaches a customer.
Red team your AI before someone else does.
Whether you are about to launch, need to validate an existing deployment, or want to understand your exposure before an audit, start with a scoping call.
(503) 389-3174 · info@sidechainsecurity.com · Portland, Oregon