Automated AI Red Teaming for Continuous AI Risk Detection
Continuously discover, verify, and exploit real vulnerabilities across your model, APIs, tools, and agent workflows and not just the model. Evidence-backed findings with severity scoring, regression re-testing, and mappings to OWASP, MITRE ATLAS, and EU AI Act/GDPR/NIST AI RMF.
- Category
- AI Security
- Registry category
- Cybersecurity & ITThreat Detection & Response
- Vendor
- HiltLock
- Location
- Not disclosed
- Reviews
- Not rated (0 reviews on the listing)
- Listed on
- AWS Marketplace
- Pricing
- 1 plan listed
Each fact carries one Provenance score: the average of its Disclosure and Evidence scores, the same rule the page uses for the overall score. Click any score to see the math behind it.
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What the publisher says
HiltLock's AI Redteaming Platform is a continuous AI security validation solution that automatically discovers, verifies, and exploits real vulnerabilities across chatbots, internal copilots, and agentic systems. It tests every layer an attacker can reach, not just the model.
The platform attacks four layers:
- the model & prompt layer (jailbreaks, system-prompt extraction, indirect injection)
- the API authorization layer (cross-tenant access testing that names the exact record reached)
- the tool & MCP layer (function-calling and Model Context Protocol abuse)
- the agentic workflow layer (privilege escalation, approval bypass, race conditions) across 14 attack families, each mapped to OWASP LLM/API/Agentic Top 10 and MITRE ATLAS.
Show the rest of the publisher’s description (5 more lines)
The engine runs a four-phase process: Recon maps system capabilities and guardrails; Exploit runs multi-turn, multi-modal attack chains across selected threat families; Verify re-runs successful attacks to confirm reproducibility; Judge scores each finding for severity with evidence checked against the actual transcript, eliminating hallucinated findings.
Every finding is delivered as an executive-ready report with clear PASS/FAIL outcomes, reproducible exploit evidence, and mappings to OWASP, MITRE ATLAS, and the regulations governing your AI - EU AI Act, GDPR, India's DPDPA, NIST AI RMF, and ISO/IEC 42001. Ship a fix and re-run the engagement: every finding returns a fixed / still-failing / regressed verdict, so you can prove the gap is actually closed.
Safety is built in, not bolted on - runs are restricted to authorized targets only, testing is non-destructive by design (no destructive tool or database actions), and an always-on SSRF guard prevents misuse.
Typical usage patterns: initial validation (2-5 evaluations), ongoing monitoring (10-20 per quarter), production-scale coverage (40+ annually). Pricing is usage-based, allowing teams to scale testing as needed.
Move from one-time testing to continuous AI security assurance.
Preview
3 imagesPlans and pricing
1 plan listed
Linked repositories
Unknown
Unknown means this listing does not publish a repository. It is not a statement that the code is closed.
Publisher resources
Sources
- Marketplace listingAWS Marketplace (opens in a new tab)
- Legal linkStandardEula (opens in a new tab)
- Company recordOutside company data, matched on hiltlock.com
Reading the score
Disclosure · how many sources corroborate it
Evidence · what kind of evidence backs it
The SettleTop steps above are possible but not automated yet, so today they sit open and a perfect 10 is out of reach. Open information like firmographics and reach has fewer steps to climb, so it sits a little higher for the same visible evidence.
Information shown here reflects the best available information at the time of assessment and may be incomplete, outdated, or subject to change. It should be independently verified before being relied upon for any decision. SettleTop is not liable for any errors, omissions, or decisions made in reliance on this information.
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