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Introduction:
The modern attack surface has expanded beyond the reach of traditional vulnerability scanners and even well-resourced bug bounty programs. As organizations ship AI-generated code and custom APIs at unprecedented speed, attackers are weaponizing frontier AI models to uncover logic-driven vulnerabilities that static signatures and human testers routinely miss. Wiz has responded with the Red Agent — an autonomous, AI-powered attacker that continuously reasons about application behavior, adapts in real time, and validates complex, multi-step exploit chains across production environments. Now generally available, the Red Agent represents a paradigm shift in offensive security: machine-speed, zero-false-positive vulnerability discovery that operates at a scale no human team can match.
Learning Objectives:
- Understand how AI-powered offensive security transcends traditional DAST, SAST, and manual penetration testing by reasoning about custom application logic
- Learn to deploy and operationalize autonomous attack surface testing using the Wiz Red Agent within cloud and API environments
- Master the technical workflow of continuous, context-aware vulnerability validation — from discovery to remediation prioritization
You Should Know:
- The Architecture of an AI-Powered Attacker: How Red Agent Reasons Like a Human Hacker
The Red Agent is not a signature-based scanner. It combines three unique advantages: deep cloud context, world-class attacker expertise encoded into its reasoning engine, and adaptive, reasoning-based exploitation. Unlike traditional tools that send predetermined payloads, the Red Agent analyzes API specifications to understand what each endpoint does, builds hypotheses from failed probes, accumulates constraints from blocked attempts, and synthesizes multi-step attack paths that only emerge from understanding how an application actually behaves.
The agent leverages frontier models including the Anthropic Claude Opus series to reason like a human attacker — but at AI speed and scale. In production, it processes over 115 billion tokens weekly across more than 150,000 web applications and APIs, identifying over 3,000 high and critical risks every week with zero false positives.
Step-by-Step: Understanding Red Agent’s Reasoning Loop
- Discovery Phase: The intelligent web crawler maps all publicly exposed assets — APIs, web applications, and misconfigured endpoints — using agentless disk analysis, cloud configuration analysis, and runtime data from the Wiz Sensor.
- Hypothesis Generation: The reasoning engine analyzes each endpoint’s function, parameters, and expected behavior to formulate potential attack vectors.
- Adaptive Probing: The agent sends dynamically crafted requests. When a request is blocked, that failure becomes a data point to narrow the solution space for the next attempt.
- Multi-Step Chaining: The agent chains attack sequences the way a skilled pentester would — for example, leveraging an SSRF vulnerability to achieve local file read on a GCP Cloud Run API.
- Validation: Every finding is accompanied by concrete proof of exploitability, impact, and business context.
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The Authorization Crisis: Why Access Control Remains the Dominant Failure Mode
The Red Agent’s production data reveals a sobering reality: authorization and access control flaws represent 54% of all unique findings. These include authentication bypasses, IDOR/BOLA (Insecure Direct Object Reference / Broken Object-Level Authorization), BFLA (Broken Function-Level Authorization), and unprotected management APIs. In one documented case, the Red Agent exploited a broken object-level authorization vulnerability in an airline’s GraphQL API, bypassing backend resolvers to expose an entire booking database in fifteen minutes.
Step-by-Step: Detecting and Mitigating BOLA Vulnerabilities
- Map API Endpoints: Use the Red Agent’s discovery phase to enumerate all API endpoints and their object references (e.g.,
/api/users/{userId}/profile). - Test Horizontal Privilege Escalation: The agent substitutes authenticated user identifiers with identifiers of other users to check for unauthorized access.
- Test Vertical Privilege Escalation: The agent attempts to access administrative functions using low-privilege credentials.
- Validate with Proof: If access is granted, the agent retrieves actual data as proof of exploitability.
- Remediate: Implement proper authorization checks at the controller level, use indirect object references, and enforce least-privilege access controls.
Linux/Windows Command Example – API Endpoint Enumeration:
Linux - Use httpx to discover API endpoints
subfinder -d example.com | httpx -silent -mc 200,201,401,403,500 | grep -E '(api|graphql|v1|v2)'
Windows - Use PowerShell to test for BOLA
$headers = @{ "Authorization" = "Bearer $env:USER_TOKEN" }
$userId = "12345"
$response = Invoke-RestMethod -Uri "https://api.example.com/users/$userId/profile" -Headers $headers
Then repeat with $userId = "12346" to test horizontal privilege escalation
3. Leaked Secrets: The High-Severity Footprint AI Uncovered
Among all exposed secrets discovered by the Red Agent, over 61% are categorized as Critical or High severity. The breakdown is alarming: 17.6% are exposed cloud credentials, 16.5% are leaked API keys, and 8.4% are other critical secrets. These leaks exponentially expand the blast radius across cloud environments, often providing attackers with direct access to production infrastructure.
Step-by-Step: Secret Detection and Rotation Workflow
- Continuous Scanning: Deploy the Red Agent to continuously scan public-facing repositories, code snippets, and error messages for secret patterns.
- Validation: The agent validates each discovered secret by attempting to use it against the corresponding service (e.g., AWS STS `GetCallerIdentity` for cloud credentials).
- Prioritization: Secrets are scored based on the sensitivity of the associated resource and the scope of access granted.
- Automated Remediation: Integrate with Wiz Workflows to automatically revoke compromised credentials and trigger rotation.
- Prevention: Implement pre-commit hooks (e.g.,
git-secrets) to prevent secret leakage at the source.
Linux Command – Scan for Secrets in a Repository:
Use truffleHog to scan for secrets in a Git repository trufflehog git https://github.com/example/repo.git --json | jq '.' Use Gitleaks for CI/CD integration gitleaks detect --source . --verbose
4. SSRF-to-Local-File-Read: A Multi-Step Attack Chain in Production
The Red Agent POV series documents how the agent uncovered a critical SSRF (Server-Side Request Forgery) vulnerability on a GCP Cloud Run API that led to local file read. The agent reasoned through the API’s functionality, identified an endpoint that accepted external URLs, and chained the SSRF with path traversal to read sensitive system files.
Step-by-Step: SSRF Exploitation and Mitigation
- Identify User-Controlled URLs: The agent maps all endpoints that accept URL parameters (e.g., `?url=https://example.com`).
- Test for SSRF: The agent sends requests to internal IP ranges (e.g., `169.254.169.254` for cloud metadata) and observes responses.
- Chain with Path Traversal: If SSRF is confirmed, the agent attempts to read local files using `file:///etc/passwd` or
file:///proc/self/environ. - Validate: The agent retrieves actual file contents as proof.
- Mitigate: Implement allowlists for allowed URLs, disable unnecessary URL schemes (
file://,gopher://), and use network-level controls to restrict outbound traffic from application servers.
Linux Command – Test for SSRF via cURL:
Test for SSRF to cloud metadata endpoint
curl -X POST https://api.example.com/fetch \
-H "Content-Type: application/json" \
-d '{"url":"http://169.254.169.254/latest/meta-data/"}'
Test for local file read
curl -X POST https://api.example.com/fetch \
-H "Content-Type: application/json" \
-d '{"url":"file:///etc/passwd"}'
5. Operationalizing Red Agent: From Discovery to Remediation
The Red Agent is now generally available as part of Wiz Attack Surface Management (ASM). Organizations can enable it through the Wiz Preview Hub and integrate it with existing security workflows. The agent doesn’t just find vulnerabilities — it provides business context, identifies resource owners, and enables automated remediation through the Green Agent.
Step-by-Step: Deploying and Operationalizing Red Agent
- Enable in Preview Hub: Navigate to `app.wiz.io/settings/preview-and-migration-hub` and enable the Red Agent.
- Define Scope: The agent automatically discovers all publicly facing assets using Wiz’s agentless disk analysis and cloud configuration data.
- Continuous Scanning: The agent runs continuously, scanning thousands of APIs and web apps without manual intervention.
- Review Findings: Each finding includes proof of exploitability, impact analysis, and business context — enabling immediate prioritization.
- Remediate: Assign findings to resource owners or leverage the Green Agent for automated investigation and fix routing.
What Undercode Say:
- Key Takeaway 1: Traditional bug bounty programs and manual penetration testing are no longer sufficient. The Red Agent’s ability to discover critical authorization flaws that eluded extensive manual research and active bug bounty programs proves that AI-powered offensive security is not just an enhancement — it’s a necessity.
- Key Takeaway 2: The dominance of access control failures (54% of findings) and leaked secrets (61% critical/high severity) reveals that organizations are systematically failing at fundamental security hygiene. AI-powered continuous testing exposes these gaps at machine speed, forcing a shift from periodic assessments to continuous validation.
Analysis: The Red Agent represents a fundamental shift in the attacker-defender dynamic. For years, defenders have relied on the asymmetry of human expertise — bug bounty hunters and pentesters finding what automated scanners miss. Frontier AI models have now inverted that asymmetry. Attackers are already weaponizing AI; defenders who fail to deploy AI-powered offensive capabilities will fall behind. The Red Agent’s zero-false-positive rate and ability to validate multi-step attack chains with concrete proof eliminate the noise that plagues traditional vulnerability management, enabling security teams to focus on what truly matters. However, this also raises a critical question: as AI-powered attackers become ubiquitous, will the security industry see a race to the bottom where only the most sophisticated AI defenses survive? The answer lies in continuous investment and operationalization — not one-time deployments.
Prediction:
- +1 The Red Agent will drive a new industry standard for continuous, AI-powered attack surface testing, forcing competitors to rapidly develop similar capabilities or risk obsolescence.
- +1 Organizations that adopt AI-powered offensive security will reduce their mean time to remediation (MTTR) for critical vulnerabilities by 60-80% within 18 months, as automated validation and context-rich findings eliminate triage bottlenecks.
- -1 The proliferation of AI-powered offensive tools will lower the barrier to entry for malicious actors, as frontier models become more accessible and weaponized for large-scale, automated exploitation campaigns.
- -1 Organizations that delay adoption of AI-powered security testing will experience a widening security gap, as attackers leverage AI to find vulnerabilities that human-led programs cannot discover at scale.
- +1 The integration of Red Agent with Wiz’s Security Graph and Green Agent will enable fully automated, closed-loop vulnerability remediation — from discovery to fix — within the next 12-24 months.
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