Evaluris

Scenario

AI & ML System Testing

Autonomous end-to-end testing of your AI attack surface.

As your organization deploys AI systems, adversaries are developing techniques to exploit them. SPECTER tests your AI attack surface end-to-end, aligned to OWASP Top 10 for LLM Applications, MITRE ATLAS, and the EU AI Act, before an attacker uses your AI systems as an entry point.

Outcomes

What you get

Autonomous testing of Foundation Models, GenAI applications, and LLM-powered endpoints against OWASP Top 10 for LLM Applications
MITRE ATLAS-mapped assessment of AI and ML system attack surfaces
MLSecOps pipeline security testing: training data integrity, model supply chain, and deployment configuration
Agentic AI and MCP (Model Context Protocol) attack surface coverage
AI supply chain assessment: third-party model dependencies, fine-tuning pipelines, and inference infrastructure
Hypothesis-to-validated findings: every reported issue carries confirmed, business-impact context
Findings mapped to MITRE ATLAS techniques and OWASP LLM categories with prioritized remediation
CISO-ready evidence for boards and regulators assessing AI governance, EU AI Act readiness, and security posture

Scope

What we assess

Prompt Injection

direct and indirect injection attacks across LLM-powered applications and agents

Insecure Output Handling

unvalidated model outputs passed to downstream systems, code interpreters, or databases

Training Data Poisoning

integrity of training pipelines and fine-tuning data sources

Model Denial of Service

resource exhaustion and availability attacks against AI inference endpoints

Supply Chain Vulnerabilities

third-party model dependencies, plugins, and integrations

Sensitive Information Disclosure

model memorization, data leakage, and inference attacks

Insecure Plugin and Tool Design

MCP server misconfigurations, agentic tool abuse, and excessive permissions

Overreliance and Excessive Agency

AI systems with overprivileged access operating without sufficient controls

MLSecOps Pipeline

CI/CD security for model training, validation, and deployment workflows

Agentic AI Attack Surface

multi-agent systems, autonomous AI pipelines, and orchestration layer security

Attack path

AI / LLM attack chain

Prompt and tool abuse move from the model boundary into connected systems.

  1. Prompt

    untrusted input

  2. Jailbreak

    policy bypass

  3. Tool call

    agent action

  4. RAG leak

    sensitive context

  5. Impact

    ATLAS mapped

Demonstration

Live mission output

Multi-step chains with real CVE correlation, MITRE ATT&CK / ATLAS mapping, and verified proof-of-concept evidence - as SPECTER surfaces them in a mission.

SPECTER: AI / ML attack surface
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Demo output (English)

Illustrative validated chains from staging engagements. CVE identifiers map to real-world threat intelligence (CISA KEV, vendor advisories). Destructive or high-impact steps (e.g. DCSync, LDAPNightmare exploitation) are shown as detected, operator-gated, or not executed where applicable.

Capabilities

Coverage at a glance

CapabilityIncluded
OWASP Top 10 for LLM Applications
MITRE ATLAS mapping
Foundation Model testing
GenAI application testing
MLSecOps pipeline assessment
Agentic AI & MCP surface testing
AI supply chain assessment
Hypothesis-to-validated findings
CISO-ready evidence output
Agent required

Why this matters now

AI systems now carry broad permissions, process sensitive user input, connect to external tools, and influence decisions across enterprise environments. A compromised LLM endpoint, a misconfigured agentic pipeline, or an exploited MCP server can provide an attacker with access that bypasses traditional security controls entirely.

Every organization deploying AI is expanding its attack surface. SPECTER maps and validates that surface before it becomes a breach path.

Autonomous AI attack-surface testing the way adversaries will: continuously, with proof, and before it matters. Aligned to OWASP LLM Top 10, MITRE ATLAS, and the EU AI Act.

Deploy Your First Mission