01
Demonstrate
We prove exploitability instead of listing possibilities.
SERVICES / AI SECURITY SERVICES
ONE PROMPT CAN TURN YOUR CHATBOT INTO AN EXFILTRATION CHANNEL.
We simulate like an attacker and report like an engineer.
- Agent, application and model security assessments
- AI red teaming that targets your guardrails
- Replayable findings, retests and continuous assessment
FROM OUR 2026 RESEARCH
31.6%
of AI-generated code samples were fully exploitable when we tested 27 AI models and coding tools.
Read the research ->METRICS & TELEMETRY
Cybersecurity research and AI model diagnostics.
real-world coding prompts in our 2026 AI code study
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ransomware attacks that utilize AI
0
%
engagements across the full AI lifecycle
0
+
years of independent, research-fueled offensive security since
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THE ATTACK SURFACE
NO SHELL REQUIRED.
RAG and tool calling turn one request into calls against files, APIs and production systems. Select a hop to see how attackers use it, and how we test it.
HOW ATTACKERS USE IT
Prompt injection, jailbreaks, obfuscated and encoded instructions, unexpected languages, long-context and multi-turn attacks.
HOW ATTACKERS USE IT
Exploiting traditional application logic, middleware bypasses, manipulating session configurations, and input validation failures to send untrusted commands.
HOW ATTACKERS USE IT
Indirect prompt injection via untrusted data feeds, system prompt leaking, and exploiting weaknesses in structural formatting of contextual instructions.
HOW ATTACKERS USE IT
Adversarial perturbations, exploiting training data biases, trigger words to bypass built-in safety alignment, and model poisoning.
HOW ATTACKERS USE IT
Poisoning the vector database context, metadata manipulation, causing unauthorized data retrieval, and bypassing document access controls.
HOW WE TEST IT
RAG pipeline architecture security audit · Vector DB access control matrix analysis
See the engagementsHOW ATTACKERS USE IT
Hijacking tools via malicious API payloads, parameter injection into external function calls, and manipulating multi-agent logic flows.
HOW WE TEST IT
Tool boundary enforcement assessment · Agent authorization chain validation
See the engagementsHOW ATTACKERS USE IT
Model weight exfiltration, container escape on inference nodes, side-channel attacks, and host level system compromise.
AI AGENT SECURITY ASSESSMENT
YOUR AGENT CAN ACT.
SO CAN ANYONE WHO TALKS TO IT.
An agent can send email, change records and run code. We try to make it do every one of those things for us, including through another agent or a poisoned tool response. For each success, we show you which control failed and recommend the fix there.
- Every action and every reachable tool is a test case
- Agent-to-agent manipulation and poisoned tool responses in scope
- Produces the third-party testing evidence AIUC-1 requires every quarter
FIND YOUR ENGAGEMENT
WHERE IS YOUR AI SYSTEM TODAY?
Fourteen engagements that follow the life of an AI system. Start wherever you are.
PROGRAM AND GOVERNANCE ASSESSMENT
Do you know what AI you run, who owns it, and what happens when it misbehaves?
An inventory of AI in use, with policies and controls assessed from acceptable use to incident response. EU AI Act issues flagged for legal review.
Mapped to NIST AI RMF
THREAT MODELING FOR AI
Where can this system be attacked, and what would the attacker gain?
Attack paths through your models, prompts, agents, plugins and suppliers, each tied to business impact.
Aligned to MITRE ATLAS
AI DEVELOPMENT LIFECYCLE REVIEW
Where is your build process open to abuse?
An end-to-end review of how models, data, prompts and tools reach production, with guardrails that fit how your teams work.
Guardrails for data and CI/CD
AI SECURE CODE REVIEW
Is the code that wraps the models as secure as the model?
Prompt construction, output handling, auth, RAG and vector DB access, tool integrations, agent orchestration, secrets and logging.
Extends your existing code review
SECURE CODING WITH AI
How do you keep AI-assisted speed without inheriting its defects?
Decide where a human sits relative to the loop,put policy and tooling in place, and trainengineers on real failure patterns.
Policy, tooling and training
TRIAGE OF AI-GENERATED FINDINGS
Your scanners report thousands of issues. Which ones are real?
Exploitability demonstrated, duplicates and false positives cleared, severity re-rated.
A short backlog you can act on
AI AGENT SECURITY ASSESSMENT
What happens if we convince your agent to do something it shouldn’t?
Every action and tool is a test case, including manipulation by another agent or a poisoned tool response.
Failed control and fix for every success
AI RED TEAMING
Can an objective-driven attackerget past your guardrails?
Prompt injection, jailbreaking, system-prompt extraction, tool abuse and exfiltration. Filters, moderation and approvals are part of the target.
Every bypass reproducible
AI APPLICATION SECURITY
Can the AI feature reach data or actions the user isn’t authorized for?
Each hop tested, from app and prompt to RAG, vector DB, tools and APIs, including tenant segregation and third-party integrations.
Devices and embedded in scope
AI MODEL ASSESSMENT
Can you trust the model to behave safely where it matters?
Unsafe output, leakage, hallucination and refusals, then hostile encoded, multilingual and multi-turn attacks. Vision and speech too.
Risk scorecard and roadmap
AI DATA AND PRIVACY
Can an attacker change what the model learns, or make it say what it shouldn’t?
Poisoning and provenance gaps on the way in. Leakage and extraction of personal data, credentials and prompts on the way out.
Inbound and outbound testing
INFRASTRUCTURE AND MODEL CUSTODY
Who can reach your model weights?
Deployment integrity, access boundaries, DoS controls, observability for anomalous use and tenant isolation.
Theft and tampering paths
RETEST AND REGRESSION HARNESS
Did the fix actually work?
Replay scripts, structured logs and pass/fail criteria with every engagement. After you fix, we run it again.
Measured change in attack success
CONTINUOUS AI ASSESSMENT
What changed since the last test?
The harness rerun on an agreed cadence and after every new model, tool or data source.
Quarterly evidence for AIUC-1
HOW WE WORK
WE DEMONSTRATE.
WE DON’T SPECULATE.
02
Verify
Results are checked across seeds and decoding settings to separate durable failures from noise.
03
Reproduce
Every issue ships with the inputs and replay steps that reproduce it, and a named owner for the fix.
04
Retest
After you fix, we rerun the same harness and measure the change in attack success.
BACKED BY RESEARCH
OUR TEST CASES COME FROM OUR RESEARCH.
RESEARCH · APRIL 2026
The Security Gap in AI-Generated Code
730 prompts, 27 models and tools. Nearly a third of samples fully exploitable.
Read the study ->WHITE PAPER · CSA AI SUMMIT2026
Husn Canaries: Defense-in-Depth for AI Coding Assistant Governance
Read the paper ->RESEARCH · MARCH 2026
Cybersecurity AI: Hacking Consumer Robots in the AI Era
Published with Alais Robotics.
Read more ->TALK · CANSECWEST 2025
AI voice clones in a real red team engagement
How an IOActive consultant usedAI-generated voices against a livetarget.
Read more ->
What’s the difference between a model, an application and an agent assessment?
Model assessments evaluate specific training flaws, bias, and extraction risks. Application assessments target standard wrapper APIs, integration points, and user prompt filters. Agent assessments focus on structural dangers in autonomous execution, tool privileges, and systemic escape paths.
Do you need our model weights or source code?
No, weights or code are not strictly required. We test in black-box configurations as well as white-box. While code and weight access speeds up identification of obscure architecture bugs, we routinely evaluate systems solely through runtime API and user interfaces.
Will this make us compliant with the EU AI Act, ISO/IEC 42001 or AIUC-1?
Our testing matrices directly align with the security requirements of the EU AI Act, ISO/IEC 42001, and draft AIUC-1 guidelines. We supply the hard technical validation required to back up audit responses and confirm strict technical alignment.
What do we get at the end?
You receive a rigorous, engineer-verified assessment report including functional proof-of-concept exploits, risk severities, clear remediation maps, and structured executive brief materials.
Can you test a third-party or SaaS AI product we use?
Yes, we analyze third-party LLMs and SaaS AI instances to trace potential data leaks, prompt injection vectors, and downstream systemic exposure in your technical ecosystem.
Do you test in production?
We typically build sandbox/staging assessments to avoid data corruption or operational disruption. Controlled testing against low-impact production paths is possible upon close operational review.
Is this instead of a penetration test?
No, it is a specialized complement. Traditional penetration testing focuses on network protocol vulnerabilities and classic injection logic. AI safety testing focuses on context boundaries, model hallucinations, adversarial prompt manipulation, and logical bypasses.
How is an engagement sized?
Sizing is determined by model complexity, integration depth of agent execution loops, data pipeline sizes, and scope requirements (white-box weights review vs purely API-driven gray-box/black-box assessments).
READY WHEN YOU ARE
WHICH SYSTEM SHOULD WE ATTACK FIRST?
Findings demonstrated with evidence you can replay, fixes assigned to a named owner, and a retest once you’ve fixed them.