Unified Zero Trust CNAPP 
Secure APIs 
Secure Kubernetes 
SIEM 
Secure Secrets 
Find AI Vulnerabilities Before Attackers Do.
Automated red teaming for LLMs, ML models, and cloud AI assets. From prompt injections to pickle exploits — continuous adversarial testing at scale.
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Supported platforms
AI Moves Fast. Attackers Move Faster.
New attack techniques emerge weekly. Point-in-time assessments leave growing gaps between tested and current state.
70%
Enterprises exposed to shadow AI breaches
22%
Annual CAGR in AI red teaming market
$28.6B
AI red teaming market by 2034
10x
Lower effort to find AI risks
RISK 01
Manual Red Teaming Cannot Scale
AI systems update faster than human testers can keep up. Automated adversarial testing closes the gap.
RISK 02
Model Supply Chain Risks
Models from Hugging Face and GitHub may contain pickle exploits, backdoors, and trojans in weights.
RISK 03
Prompt Injection & Jailbreaking
LLMs manipulated to bypass guardrails, reveal system prompts, or execute unauthorized actions.
RISK 04
Hallucination & Misinformation
False assertions, fabricated packages, and misleading outputs damage trust and create legal liability.
RISK 05
No Continuous Assessment
Most organizations test only at deployment. Models change, configs drift — gaps grow silently.
RISK 06
Cloud AI Misconfigurations
SageMaker, Bedrock, Azure OpenAI endpoints with excessive permissions and public exposure.
RISK 07
Regulatory Pressure Mounting
EU AI Act, NIST AI RMF, ISO 42001 require documented adversarial testing evidence.
RISK 08
Shadow AI & Unvetted Models
Teams deploy models without security review, creating blind spots where vulnerable models operate.
RISK 09
Agentic AI Attack Surface
Multi-agent systems and MCP servers create cascading attack paths across entire systems.
Three Attack Surfaces. One Platform For Adversarial Red Teaming Against AI Models.
Red team LLMs, scan ML model artifacts, and assess cloud AI infrastructure — all from a single console.
LLM Adversarial Probing
1,500+ probes across prompt injection, hallucination, toxicity, and code safety. OpenAI, Ollama, and custom endpoints.
Grandma jailbreak, TAP, encoding bypass, suffix attacks
Package hallucination — PyPI, npm, RubyGems, Crates
Google Perspective API, Roberta, NLI contradiction
Cron scheduling, custom prompts, regression re-scans
ML Model Static Analysis
Scan serialized models for insecure deserialization, embedded risks, and supply chain exposure from public repositories.
Pickle, HDF5/H5, TensorFlow SavedModel, ONNX
GitHub PAT + Hugging Face token integrations
Arbitrary code execution via deserialization
Malicious Lambda layers, tampered weights, backdoors
Cloud AI Assessment
Discover and continuously monitor cloud AI workloads across AWS, Azure, and GCP for misconfigurations and excessive permissions.
AWS Bedrock, SageMaker endpoint scanning
Azure OpenAI, Azure ML, Copilot Studio
GCP AI Platform permission review
Public exposure, missing encryption, IAM excess
See It In Action.
A single console for adversarial probing, model scanning, cloud assessment, and compliance reporting.
Red Teaming Dashboard
Simplified Collector Based Multi-Source Scanning
AI Assisted Remediation
Pipeline View for Your Datasets, Compute, Applications and LLM Deployments
Detailed Red Teaming Findings for each LLM/ML Model
Findings Detail Screen
1,500+ Adversarial Probes.
Four Categories of ML/LLM Scans.
Predefined and custom prompts simulate real attacker techniques across every known LLM weakness.
| Category | What it tests | Key probes |
|---|---|---|
| Prompt Injection | Bypass guardrails via social engineering, encoding, latent instructions | Grandma, DoNotAnswer, Base64/16/32, Latent, Suffix, TAP, XSS |
| Hallucination | False assertions, fabricated packages, snowball contradictions | False Assertion, Snowball Primes, Package Hallucination (Py/JS/Rb/Rust) |
| Toxicity | Coerce harmful, abusive, or sexually explicit outputs | RealToxicityPrompts (8 categories × 50), Bullying, Profanity (11 sub-cat) |
| Code Safety | Generate malware signatures, phishing content, evasion code | GTphish, GTUBE, EICAR, Malware Gen (208 prompts across 4 classes) |
Security From Probe → Finding → Fix
AccuKnox Red Teaming Engine runs adversarial probes, evaluates with multiple detectors, and produces actionable findings with remediation guidance.
From Pre-Deployment to Post-Incident.
AI red teaming across the full lifecycle — before, during, and after production deployment.
LLM Red Teaming
Automated adversarial probing across hallucination, toxicity, prompt injection, and code safety.
ML Model Scanning
Static analysis of model files from GitHub and Hugging Face. Detect pickle exploits, supply chain attacks.
Cloud AI Assessment
Discover and monitor cloud AI resources across AWS, Azure, and GCP for misconfigurations.
Pre-Deployment Validation
Sandbox-driven assessments evaluate safety, bias, toxicity, and jailbreak resilience before production.
AI Agent Security Testing
Red team agentic AI, MCP tools, and multi-agent architectures for cascading vulnerabilities.
Continuous Compliance
Ongoing adversarial testing produces documented evidence for EU AI Act, NIST AI RMF, ISO 42001.
Supply Chain Security
Ensure models from public repositories are safe. Scan for malicious payloads, backdoors, trojans.
Incident Validation
Re-run targeted red teaming to verify remediation effectiveness and prevent regression.
Don't Just Settle For An AI Security Platform, Get Complete Security Coverage
While the market consolidates under network vendors, AccuKnox delivers full-spectrum AI red teaming without lock-in.
Full-Spectrum: LLM + ML + Cloud
The only platform that red teams all three attack surfaces in a single product. Competitors cover one or two.
Independent & Vendor-Neutral
While Robust Intelligence → Cisco, Protect AI → Palo Alto, CalypsoAI →F5 — AccuKnox remains independent. No lock-in.
Continuous, Not Point-in-Time
Always-on adversarial testing with cron scheduling, automated re-scans, and dynamic risk scoring that updates as models change.
Closed-Loop Runtime Defense
Red teaming findings feed directly into Prompt Firewall, ModelArmor,and AI-DR. Vulnerabilities automatically inform runtime policies.
Zero Trust Heritage
Built on KubeArmor's eBPF enforcement. Red teaming results generate least-permissive policies enforced at the kernel level.
Attacker-Style Evidence
Every finding includes the exact prompt, model output, goal attempted, and detector verdict. Courtroom-ready for compliance audits.
CNAPP Integration
AI red teaming embedded within a broader Cloud-Native Application Protection Platform — single pane of glass for cloud and AI security.
AI-Assisted Remediation
Every finding comes with AI-generated remediation steps. Batch Ask AI processes multiple findings simultaneously.
Red Teaming and ML/LLM Scanning – Our Differentiators
Full-spectrum coverage, independence, and closed-loop remediation set AccuKnox apart in a consolidating market.
| Capability | Vendor A, B, C |  |
|---|---|---|
| LLM adversarial probing |  |  1,500+ probes, 4 categories |
| ML model static analysis | | Pickle, HDF5, TF, ONNX scanning |
| Cloud AI infrastructure scanning | | AWS, Azure, GCP AI asset assessment |
| Continuous automated re-scans | | Cron-based, always-on |
| Runtime defense integration | | Prompt Firewall, ModelArmor, AI-DR |
| Kernel-level enforcement | | eBPF + KubeArmor policies |
| AI-assisted remediation | | Per-finding + Batch Ask AI |
| CNAPP integration | | CSPM, CWPP, KSPM unified |
| Custom prompt support | | Upload custom JSON, mix with defaults |
Red Teaming That Actually Checks Your Compliance and Benchmarks Your Score
Every red teaming finding maps to compliance frameworks — producing the documented evidence regulators require.
EU AI Act
Documented adversarial testing evidence for high-risk AI systems. Fines up to €30M / 6% global revenue.
NIST AI RMF
Risk assessment documentation. Adversarial testing across GOVERN, MAP, MEASURE, MANAGE functions.
ISO 42001
AI management system compliance with evidence of continuous security assessment.
OWASP Top 10 for LLM
Direct coverage of injection, data leakage, supply chain, and output handling vulnerabilities.
MITRE ATLAS
Adversarial threat landscape mapping with technique-level coverage documentation.
AI Vulnerability Database
Automatic mapping of every finding to AVID entries for standardized vulnerability reporting.