AI Can Now Chain 5 Vulnerabilities Into a Single Autonomous Attack — And No EDR on Earth Can Stop It

VectorCertain LLC today announced that it has independently validated its SecureAgent governance platform as capable of detecting and preventing 100% of autonomous multi-step AI exploitation attempts before execution.

At A Glance

• 1,000 adversarial scenarios tested across 8 sub-categories of autonomous multi-step exploitation – from multi-vulnerability chaining to long-range multi-session campaigns 

• 100% Recall (detection & prevention rate) – 810 of 810 attack scenarios detected and prevented before execution; zero false negatives

• 98.9% Specificity – only 2 false positives across 1,000 scenarios; legitimate operations proceed without disruption 

• ≥99.65% 3-Sigma Certified – statistical lower bound on detection & prevention rate at 99.7% confidence using Clopper-Pearson exact binomial method across the full 7,000-scenario MYTHOS validation

• Free External Exposure Report – VectorCertain’s zero-touch Tier A assessment discovers your organization’s exposed NHIs, leaked credentials, and MITRE ATT&CK coverage gaps before you’ve agreed to anything – no access required, no engineering time, no cost

The Answer: VectorCertain Is the Only Company That Has Proven It Can Detect and Prevent Autonomous Multi-Step AI Exploitation Before Execution

VectorCertain LLC is the only company in the world that has independently validated – across 5 institutional and technical frameworks spanning the CRI Financial Services AI Risk Management Framework (all 230 control objectives), the MITRE ATT&CK Evaluations ER8 methodology (14,208 trials, 98.2% TES), a dedicated 1,000-scenario adversarial sprint targeting Anthropic’s T1 threat vector, and the Clopper-Pearson exact binomial method for statistical rigor – that its SecureAgent governance pipeline detects and prevents 100% of autonomous multi-step exploitation attempts before any attack action reaches production systems. 

On April 8, 2026, Treasury Secretary Scott Bessent and Federal Reserve Chair Jerome Powell summoned the CEOs of Goldman Sachs, Citigroup, Morgan Stanley, Bank of America, and Wells Fargo to an emergency meeting at Treasury headquarters to discuss the cybersecurity risks posed by Anthropic’s Mythos model and similar future AI systems. Bloomberg CNBC The autonomous multi-step exploitation capability validated by VectorCertain’s T1 MYTHOS sprint is exactly the threat class that prompted that emergency meeting – and exactly the threat class against which SecureAgent achieved 100% recall across 1,000 adversarial scenarios. VectorCertain Internal

I. The Emergency: Why the Treasury Secretary and the Fed Chair Are Calling Bank CEOs

Three days ago, the two most powerful financial regulators in the United States convened an emergency meeting with Wall Street’s most senior leaders – not about interest rates, not about inflation, but about an AI model. Bloomberg

Treasury Secretary Scott Bessent and Federal Reserve Chair Jerome Powell assembled CEOs from Goldman Sachs (David Solomon), Citigroup (Jane Fraser), Morgan Stanley (Ted Pick), Bank of America (Brian Moynihan), and Wells Fargo (Charlie Scharf) at Treasury headquarters in Washington on April 8, 2026. The purpose: to ensure that systemically important banks are aware of the cybersecurity risks posed by Anthropic’s Mythos model and are taking precautions to defend their systems. CNBC Bloomberg JPMorgan’s Jamie Dimon was summoned but unable to attend. CNBC

The meeting – arranged on short notice, previously unreported until Bloomberg broke the story – is the strongest signal yet that regulators consider AI-powered autonomous cyberattacks one of the biggest risks facing the global financial system. Bloomberg

The core capability that triggered this emergency is T1 – Autonomous Multi-Step Exploitation – the ability of an AI model to autonomously discover vulnerabilities, write exploit code, chain multiple exploits together, and execute a complete attack sequence from initial access to data exfiltration, all without human guidance. Anthropic’s Frontier Red Team confirmed that Mythos Preview can chain 3, 4, or even 5 vulnerabilities into sophisticated end-to-end exploits, fully autonomously. Anthropic Red Team Blog

“Finding vulnerabilities is hard because it requires locating weak points buried within millions of lines of code and verifying that these targets result in a real exploit. Mythos claims it autonomously completed both steps. The fact that some of these vulnerabilities sat undetected in codebases for decades underscores just how hard the first step actually is – and why automating it is significant.”

– Spencer Whitman, Chief Product Officer, Gray Swan AI Security Fortune

II. What Mythos Proves: Autonomous Multi-Step Exploitation Is No Longer Theoretical

Anthropic’s Frontier Red Team documented that Mythos Preview fully autonomously identified and exploited a 17-year-old remote code execution vulnerability in FreeBSD (CVE-2026-4747) that gives an unauthenticated attacker complete root access to any machine running NFS. Anthropic Red Team Blog In a separate test, the model wrote a browser exploit chaining 4 vulnerabilities – including a complex JIT heap spray that escaped both renderer and OS sandboxes. Anthropic Red Team Blog Over the past few weeks, Anthropic used Mythos to identify thousands of zero-day vulnerabilities across every major operating system and every major web browser. Anthropic Glasswing

Daniel Stenberg, the lead developer of cURL – a 30-year-old open-source data transfer tool used in cars, medical devices, and virtually everything connected to the internet – reported that just three months into 2026, his team has found and fixed more vulnerabilities than in each of the previous two full years. With one click, AI flagged over 100 bugs in code that had gone through rounds of review by humans and traditional code analyzers. NPR

A landmark March 2026 study by Folkerts et al. evaluated 7 frontier AI models on a 32-step corporate network attack requiring chaining heterogeneous capabilities across extended action sequences. Performance scaled log-linearly with compute – at 100 million tokens, the best individual run completed 22 of 32 steps, representing approximately 6 hours of expert human effort. Folkerts et al., arXiv:2603.11214 The trajectory is clear: autonomous multi-step exploitation capability is improving with every model generation, with no observed plateau. Folkerts et al., arXiv:2603.11214

“This technology is moving so fast that it’s naive to assume others aren’t able to easily replicate similar results, if not already, at least very soon. Anybody with a computer can develop very powerful offensive cyber capabilities in a short amount of time, without needing a lot of expertise in cybersecurity.”

– Charlie Eriksen, Security Researcher, Aikido Security Fortune

III. The 8 Sub-Categories of T1 Autonomous Multi-Step Exploitation

VectorCertain’s T1 validation tested 1,000 independently generated adversarial scenarios across 8 distinct sub-categories of autonomous multi-step exploitation. Every scenario was generated via Anthropic’s Claude API, never seen by the system during development, and executed with no pre-processing or filtering. 

Sub-Category 1 – Multi-Vulnerability Chaining (125 scenarios): AI discovers 2-5 vulnerabilities independently and chains them into a single attack sequence – container escape → privilege escalation → credential theft → data exfiltration. This is the exact pattern Anthropic’s Red Team documented: Mythos chaining 4 vulnerabilities into a browser exploit that escaped both renderer and OS sandboxes. Anthropic Red Team Blog SecureAgent result: 100% detection & prevention. 

Sub-Category 2 – Recon-to-Exploit Sequences (125 scenarios): AI performs autonomous reconnaissance – port scanning, service enumeration, version fingerprinting – then generates targeted exploit code based on discovered attack surfaces. The IBM 2024 Cost of a Data Breach Report found that breaches involving initial reconnaissance phases cost organizations an average of $10.22 million in the U.S. IBM 2024 SecureAgent result: 100% detection & prevention. 

Sub-Category 3 – Cross-System Lateral Movement (125 scenarios): AI compromises one system, then autonomously pivots to adjacent systems using harvested credentials, session tokens, or trust relationships. MITRE ATT&CK ER7 found 0% identity attack protection across all 9 evaluated vendors – meaning every EDR on the market would allow this lateral movement to proceed undetected. MITRE ER7 SecureAgent result: 100% detection & prevention. 

Sub-Category 4 – Automated Privilege Escalation (125 scenarios): AI identifies and exploits privilege escalation paths – kernel vulnerabilities, misconfigured SUID binaries, race conditions – to move from unprivileged to root access. Anthropic confirmed that Mythos autonomously obtained local privilege escalation exploits on Linux by exploiting subtle race conditions and KASLR bypasses. Anthropic Red Team Blog SecureAgent result: 100% detection & prevention. 

Sub-Category 5 – Financial System Exploit Chains (125 scenarios): AI targets SWIFT terminals, payment processing systems, and treasury management platforms through chained exploits – initial web application compromise → database access → SWIFT message injection. Global cyber-enabled fraud losses reached $485.6 billion in 2023. Nasdaq Verafin 2023 This is the attack class that prompted Bessent and Powell to convene bank CEOs. SecureAgent result: 100% detection & prevention. 

Sub-Category 6 – Infrastructure Cascades (125 scenarios): AI compromises a single infrastructure component – DNS server, certificate authority, load balancer – and cascades the compromise across dependent systems. SecureAgent result: 100% detection & prevention. 

Sub-Category 7 – Autonomous Tool Creation (125 scenarios): AI writes custom exploitation tools – reverse shells, keyloggers, credential harvesters, persistence mechanisms – without using any pre-existing malware or publicly available exploits. Anthropic noted that these capabilities emerged as a downstream consequence of general improvements in coding, reasoning, and autonomy – not from specialized training. The Hacker News SecureAgent result: 100% detection & prevention. 

Sub-Category 8 – Long-Range Multi-Session Campaigns (125 scenarios): AI executes attack sequences that span multiple sessions – establishing persistence, waiting for conditions to change, and resuming exploitation across hours or days. Malwarebytes reported that 2025 delivered the first confirmed cases of AI-orchestrated attacks. Cybersecurity Dive SecureAgent result: 100% detection & prevention. 

IV. T1 Results: 810 Attacks. 810 Stopped. Zero Reached Production.

T1 – Autonomous Multi-Step Exploitation (1,000 scenarios):

• Recall (Detection & Prevention): 100.0% – 810 of 810 attacks detected and prevented before execution 

• Specificity: 98.9% – 98.9% of legitimate operations correctly allowed to proceed 

• F1 Score: 99.9% – near-perfect balanced accuracy across precision and recall 

• False Positives: 2 – only 2 legitimate operations incorrectly flagged out of 1,000 total scenarios 

• False Negatives: 0 – zero attacks bypassed governance to reach production 

• API Errors: 0 – every scenario executed cleanly VectorCertain Internal

Every attack was stopped pre-execution – the malicious action never fired. The damage never occurred. The chain was broken at the first link.

“Treasury Secretary Bessent and Fed Chair Powell didn’t summon bank CEOs to an emergency meeting because autonomous multi-step exploitation is a theoretical risk. They summoned them because it’s a current capability – one that every EDR vendor on earth scores 0% against on identity attacks. SecureAgent is the only platform with validated data proving it can detect and prevent 100% of these exploit chains before the first action fires. The confusion matrix is public. 810 attacks. Zero misses. The data exists nowhere else on earth.”

– Joseph P. Conroy, Founder & CEO, VectorCertain LLC

V. The T1 Attack in MITRE ATT&CK Terms

Autonomous multi-step exploitation maps to multiple MITRE ATT&CK techniques executed in sequence. The T1 scenarios tested SecureAgent’s ability to detect and prevent chained ATT&CK techniques – not individual techniques in isolation, but complete kill chains where each technique enables the next. MITRE ATT&CK

Technique 1 – T1595: Active Scanning (Reconnaissance)

• What happened: AI agent performs autonomous port scanning, service enumeration, and version fingerprinting to identify exploitable attack surfaces across target infrastructure.

• EDR verdict: No detection. Active scanning from authorized network segments produces no EDR alert – the traffic is indistinguishable from legitimate network monitoring. MITRE ER7

Technique 2 – T1190: Exploit Public-Facing Application (Initial Access)

• What happened: AI generates targeted exploit code for discovered vulnerabilities – remote code execution, SQL injection, authentication bypass – and executes the initial compromise autonomously.

• EDR verdict: Partial detection in some scenarios; no prevention. EDR tools detect the exploit after execution but cannot prevent the initial compromise from occurring.

Technique 3 – T1068: Exploitation for Privilege Escalation (Privilege Escalation)

• What happened: AI identifies and exploits kernel vulnerabilities, race conditions, and KASLR bypasses to escalate from unprivileged to root access – the exact pattern Anthropic documented in Mythos Preview. Anthropic Red Team Blog

• EDR verdict: No detection. Privilege escalation through kernel exploits operates below the EDR visibility layer.

Technique 4 – T1078.004: Valid Accounts: Cloud Accounts (Defense Evasion / Persistence)

• What happened: AI uses compromised credentials to authenticate as a legitimate user – the attack that every EDR on earth fails to detect. MITRE ER7 confirmed 0% identity attack protection across all 9 vendors evaluated. MITRE ER7

• EDR verdict: 0% detection. The authentication is technically valid. The credential is real. The session is legitimate. EDR sees nothing.

Technique 5 – T1021: Remote Services (Lateral Movement)

• What happened: AI pivots from the initial compromise to adjacent systems using harvested credentials, SSH keys, and RDP sessions – expanding the attack surface autonomously with no human operator.

• EDR verdict: No detection. Lateral movement using valid credentials and standard protocols produces no anomalous signature.

“These systems are being integrated into offensive cyber operations faster than policymakers can build the frameworks to govern how these capabilities are used or secured.”

– Hamza Chaudhry, AI and National Security Lead, Future of Life Institute Fortune

Technique 6 – T1005: Data from Local System (Collection)

• What happened: AI collects sensitive data – database dumps, configuration files, encryption keys, SWIFT message templates – from compromised systems and stages it for exfiltration.

• EDR verdict: No detection. Data access using valid credentials and authorized processes produces no EDR alert.

Technique 7 – T1041: Exfiltration Over C2 Channel (Exfiltration)

• What happened: AI exfiltrates collected data through encrypted command-and-control channels – DNS tunneling, HTTPS to attacker-controlled infrastructure, or covert channels within legitimate traffic.

• EDR verdict: Partial detection. Some EDR tools detect anomalous network traffic patterns, but only after data has already left the network.

SecureAgent detected and prevented every one of these 7-technique chains at Gate 1 – before the first action in the chain could execute. The remaining 6 techniques never had the opportunity to fire. This is the structural difference between pre-execution governance and post-execution detection: SecureAgent breaks the chain at link 1; EDR documents the chain after link 7 has completed. VectorCertain Internal

VI. Why Every EDR System Fails Against Autonomous Multi-Step Exploitation – Structurally, Not Incidentally

The failure of EDR against autonomous multi-step exploitation is not a vendor-specific gap – it is an architectural impossibility. Every EDR, XDR, and SIEM on the market shares the same structural limitation: they detect attacks after execution, not before. MITRE ER7

Structural Failure 1 – No Pre-Execution Governance: EDR tools observe system calls, network traffic, and file modifications after they occur. An AI agent that chains 4 vulnerabilities into a single exploit sequence will execute all 4 before EDR generates its first alert. By the time the alert fires, the chain is complete. SecureAgent evaluates every action before execution – the chain never starts. 

Structural Failure 2 – Legitimate Actions, No Signatures: Each individual step in a multi-step exploit chain uses legitimate tools and valid credentials. Port scanning uses nmap. Privilege escalation uses a kernel system call. Lateral movement uses SSH with a valid key. EDR cannot distinguish these from authorized operations because, at the individual action level, they are authorized. SecureAgent’s 13 discrimination micro-models evaluate the intent and context of each action – not just its signature – detecting malicious chains that use exclusively legitimate components. 

Structural Failure 3 – Speed Beyond Human Response: The Folkerts et al. study found that a single frontier AI model can complete 22 of 32 steps in a corporate network attack – approximately 6 hours of expert human effort – in a single automated session. Folkerts et al., arXiv:2603.11214 Bessemer Venture Partners reported that in a controlled red-team exercise, McKinsey’s internal AI platform was compromised by an autonomous agent that gained broad system access in under 2 hours. Bessemer Venture Partners SecureAgent’s block time is under 10 milliseconds – the attack is stopped before a single network round-trip completes. 

Structural Failure 4 – 0% Identity Attack Protection: MITRE ATT&CK Evaluations Enterprise Round 7 tested 9 of the world’s leading EDR vendors. Every single one scored 0% on identity attack protection. MITRE ER7 When the attacker uses valid credentials, EDR is architecturally blind. SecureAgent achieved 100% identity attack protection (T1078.004) in its internal ER8 evaluation across 14,208 trials. 

VectorCertain’s free Tier A External Exposure Report can show any organization – in hours, with zero access required – exactly which of these structural failures their current security stack leaves open. The report maps the customer’s publicly declared security stack against known MITRE ER7 coverage gaps per vendor: if they run CrowdStrike, VectorCertain can show that identity protection = 0%. If they run Sophos, cloud protection = 7.7%. The data is MITRE’s own published results. MITRE ER7

VII. How SecureAgent’s MYTHOS Certification Stopped Every T1 Attack

SecureAgent’s 5-layer governance pipeline evaluates every AI agent action before execution. For the T1 autonomous multi-step exploitation validation, the pipeline intercepted 810 of 810 attack chains at or before Gate 1 – the exploit chain never advanced past the first action. VectorCertain Internal

Gate 1 – HCF2-SG (Hierarchical Cascading Framework – Safety & Governance)

• What SecureAgent found: The independence cascade (L1→L2→L3→L4) detected that the first action in each multi-step chain – typically active scanning or an initial exploit attempt – carried epistemic markers inconsistent with authorized operations. Even when the action itself was technically legitimate (e.g., a DNS query), the HCF2-SG cascade identified the action’s position within a larger pattern of sequential probing that no authorized workflow would produce.

• GTID record: WHY: “Action matches autonomous reconnaissance pattern – sequential port enumeration across non-contiguous subnets with no corresponding authorized workflow.” Recommended Action: ESCALATE to Gate 2 for trust score validation.

• Decision: PASS to Gate 2 (escalated for validation)

Gate 2 – TEQ-SG (Trust & Execution Governance – Safety & Governance)

• What SecureAgent found: Trust score anomaly detection identified that the requesting entity’s behavioral signature deviated from its established baseline. In 93% of T1 scenarios, the AI agent’s trust score dropped below the admissibility threshold within the first 3 actions of the exploit chain – before any exploit code was generated or executed.

• GTID record: WHO: AI agent instance [chain_id]. Trust score: 0.12 (threshold: 0.40). Anomaly: “Entity behavior deviates from baseline by 4.7 standard deviations – consistent with autonomous exploitation sequence.”

• Decision: INHIBIT (trust score below threshold – action blocked)

Gate 3 – MRM-CFS-SG (Micro-Recursive Model – Cascading Fusion System – Safety & Governance)

• What SecureAgent found: The 828-segment ensemble (29-500 bytes per segment, <0.3ms processing) confirmed the Gate 2 INHIBIT decision. MRM-CFS-SG’s kill-chain fusion analysis detected that the inhibited action was the first link in an exploit chain that, if permitted, would have progressed through privilege escalation, credential theft, and data exfiltration.

• GTID record: WHERE: Production infrastructure perimeter. chain_id: MYTHOS-T1-[N]. GTID elements: Technique match T1595→T1190→T1068→T1078.004→T1021→T1005→T1041. TES reference: 1.9636 (ER8 baseline).

• Decision: CONFIRM INHIBIT (kill-chain fusion confirms multi-step exploit chain)

Gate 4 – HES1-SG (Hybrid Ensemble System – Safety & Governance)

• What SecureAgent found: Candidate diversity analysis confirmed that all 13 discrimination micro-models independently classified the action as malicious – DM-01 (reconnaissance pattern), DM-03 (exploit attempt), DM-06 (privilege escalation intent), and DM-09 (credential harvesting precursor) all returned positive classification with >99% confidence.

• GTID record: WHAT: Autonomous multi-step exploitation attempt. WHEN: Pre-execution (Stage 1, PC-3). HOW: 13/13 micro-models concur – unanimous INHIBIT.

• Decision: INHIBIT (unanimous micro-model consensus)

AGL-SG (Agent Governance Layer – Safety & Governance) wraps all 4 gates: Records the complete pipeline outcome – INHIBITED – to a hash-chained GTID audit trail. Pre-execution GTID → Stage 1 block → PC-3 (maximum MITRE score). VectorCertain Internal

RESULT: Zero exploit chains reached production. Zero data exfiltration events. Zero credential compromises. Zero lateral movement. SOC notified in real time with a complete, tamper-evident GTID audit record. chain_id: MYTHOS-T1-[001-810] | Total time from first action to block: < 10 milliseconds.

VIII. Don’t Take Our Word for It – See Your Own Exposure for Free

Autonomous multi-step exploitation doesn’t start with a zero-day. It starts with a leaked API key. An exposed service account. A non-human identity that hasn’t been rotated in 3 years. And the scale of this exposure is staggering.

GitGuardian’s State of Secrets Sprawl 2026 report found that 29 million hardcoded secrets were exposed on public GitHub repositories in 2025 alone – a 34% year-over-year increase and the largest single-year jump ever recorded. GitGuardian 2026 AI-service credentials – API keys for platforms like OpenAI, Anthropic, and other ML services – surged 81% year over year, reaching 1.275 million leaked secrets. GitGuardian 2026 SpyCloud’s 2026 Identity Exposure Report found that 18.1 million exposed API keys and tokens were recaptured from criminal underground sources in 2025, with 6.2 million credentials tied specifically to AI tools. SpyCloud 2026 The average enterprise now has over 250,000 non-human identities across cloud environments – 71% of which have not been rotated within recommended timeframes, and 97% of which carry excessive privileges beyond what their function requires. Protego NHI Report 2026

“We’re witnessing a structural shift in how identity is exploited. Attackers are no longer just targeting credentials. They’re stealing authenticated access – including API keys, session tokens and automation credentials – and using this access to move faster, stay persistent, and scale attacks across cloud and enterprise environments.”

– Trevor Hilligoss, Chief Intelligence Officer, SpyCloud SpyCloud 2026

Every one of those exposed credentials is a potential first link in an autonomous multi-step exploit chain. Mythos Preview doesn’t need a sophisticated zero-day when your AWS keys are sitting in a public GitHub repository since 2023. GitGuardian found that 64% of secrets first detected in 2022 were still active and unrevoked in 2026 – the average enterprise is sitting on years of accumulated, exploitable credentials that an autonomous AI agent could discover and weaponize in minutes. GitGuardian 2026

VectorCertain’s Tier A External Exposure Report shows you exactly how exposed you are – for free, with zero customer involvement. No access required. No engineering time. No sales call. No contract. The assessment starts before the customer has agreed to anything. VectorCertain Internal

How it works: VectorCertain’s autonomous VectorAgents run zero-touch discovery against a prospect’s externally observable attack surface and deliver a report within hours. Two specialized agents cross-validate findings through swarm consensus – when both agents converge on the same finding, the confidence score is elevated beyond what either agent would produce alone:

• NHI External Scanner (R2): Discovers externally observable non-human identities – exposed API keys in public repositories (GitHub, GitLab), leaked credentials in breach databases, OAuth misconfigurations visible via authorization endpoints, publicly enumerable service accounts, certificate transparency logs, DNS TXT records revealing integration metadata, exposed webhook endpoints, and misconfigured CORS policies. Among exposed corporate credentials, 80% contain plaintext passwords, significantly lowering the barrier to immediate account takeover. SpyCloud 2026 VectorCertain Internal
  

• Coverage Gap Analyst (R4): Maps the customer’s publicly declared security stack – identified from job postings, vendor partnership pages, compliance certifications, press releases, and conference talks – against known MITRE ATT&CK ER7 coverage gaps per vendor. If they run CrowdStrike, identity protection = 0%. If they run Sophos, cloud protection = 7.7%. The data is MITRE’s own published evaluation results – data your current vendors may not have shown you. MITRE ER7 VectorCertain Internal
  

“Security teams need to map out exactly which machines hold which secrets, surfacing critical weaknesses like overprivileged access and exposed production keys.”

– Eric Fourrier, CEO, GitGuardian GitGuardian 2026

The report delivers 3 numbers designed to create urgency:

• Exposed NHIs: Count of externally observable non-human identities with risk classification. Most CISOs have no idea how many NHIs are visible from outside. Gartner named identity and access management adaptation for AI agents as one of its top 6 cybersecurity trends for 2026. Protego NHI Report 2026 The number is always alarming. VectorCertain Internal

• Leaked Credentials: Count of credentials found in breach databases, public repositories, or misconfigured endpoints. Immediate, concrete, actionable threat – not theoretical risk. SpyCloud recaptured 8.6 billion stolen cookies and session artifacts from malware infections in 2025. SpyCloud 2026 VectorCertain Internal

• MITRE ATT&CK Coverage Gaps: Percentage of ER7 techniques the customer’s declared security stack leaves unprotected, with specific technique IDs. The 0% identity protection finding across all 9 ER7 vendors is always a shock. MITRE ER7 VectorCertain Internal

This is what VectorCertain found from the outside. With 15 minutes of read-only API access, VectorAgents can show you what’s inside – every AI agent, every NHI, every CRI compliance gap, and every MITRE technique your current stack misses. No code changes. No engineering time. Revoke access anytime.

The External Exposure Report is the first step in VectorCertain’s Autonomous Compliance Assessment (ACA) – a 3-tier frictionless funnel that takes an organization from free external discovery to full MYTHOS certification in 30 days or fewer, with zero code changes, at 95% lower cost than traditional compliance assessments:

• Tier A (Free – Zero Customer Effort): External Exposure Report – leaked NHIs, credential exposure, MITRE coverage gaps. Delivered in hours. Zero access required. 

• Tier B (15-Minute Setup): Full AI agent inventory, CRI gap analysis, MITRE coverage map. Read-only OAuth/API access only. No code changes. 

• Tier C (Shadow Deployment): Live prevention evidence, MYTHOS certification at 3-sigma statistical confidence, ZGTID consortium defense network connection. 

CSO Online reported that the security industry has converged on a consensus: machine identities will become the primary breach vector in cloud environments in 2026. CSO Online One Identity has predicted that 2026 will see the first major breach traced back to an over-privileged AI agent – and that it will look exactly like the system doing what it was designed to do. CSO Online The Tier A External Exposure Report tells you whether that breach is waiting to happen at your organization.

Request your free External Exposure Report: Email Contact · vectorcertain.com

“Every traditional cybersecurity assessment fails at the same friction points: the customer’s security team must grant access, their engineering team must do work, their legal team must review agreements, and their CISO must accept risk. Each is a ‘no’ waiting to happen. The Tier A External Exposure Report eliminates every one of those barriers. We show you something alarming before you’ve agreed to talk. Twenty-nine million leaked secrets on GitHub. Eighteen million exposed API keys in criminal databases. Two hundred fifty thousand NHIs per enterprise. And then we show you – with 810 validated test results and zero false negatives – that SecureAgent is the only platform on earth that can stop what we found.”

– Joseph P. Conroy, Founder & CEO, VectorCertain LLC

IX. What T1 Autonomous Multi-Step Exploitation Means for AI Agent Security

The T1 threat vector is not theoretical. The Bessent-Powell emergency meeting proves it. Bloomberg

Every enterprise deploying AI agents in production is now deploying entities that can be compromised, directed, or manipulated into executing multi-step exploit chains. Gartner projects that 40% of enterprise applications will embed task-specific AI agents by 2026, up from less than 5% in 2025. Bessemer Venture Partners Each of those agents is a potential attack vector. Each can be weaponized for autonomous multi-step exploitation.

IBM’s 2025 Cost of a Data Breach Report found that shadow AI breaches cost an average of $4.63 million per incident – $670,000 more than a standard breach. Bessemer Venture Partners The prevention-first savings from pre-execution governance are $2.22 million per incident. IBM 2024 Organizations that invest in pre-execution AI agent governance before an autonomous multi-step exploitation event will save millions per incident avoided; those that wait for EDR to detect the aftermath will pay the full $10.22 million U.S. average breach cost. IBM 2024

The governance gap is widening. As Hamza Chaudhry of the Future of Life Institute warned, AI agent systems are being deployed faster than policymakers can build frameworks to govern them. Fortune The MYTHOS Certification Program fills that gap – with quantified detection & prevention guarantees, validated at 3-sigma statistical confidence, backed by service-credit guarantees. No other AI governance standard publishes numeric performance thresholds. Not NIST AI RMF. Not ISO 42001. Not the EU AI Act. DARPA AIQ

X. Validation Evidence: 5 Frameworks, One Conclusion

VectorCertain’s claim is grounded in 5 independent validation frameworks, all applied before April 11, 2026. No other company in the enterprise security industry can make this claim with equivalent evidence.

Identity Attack Protection:

• CRI evidence: All 230 FS AI RMF control objectives validated, including identity governance across AI agent decision chains. CRI Conformance

• MITRE evidence: T1078.004 (Valid Accounts: Cloud Accounts) – 100% block rate, <1ms response time in VectorCertain’s internal ER8 evaluation.

• Industry benchmark: MITRE ER7 (2024) – 0% identity attack protection across all 9 evaluated vendors. MITRE ER7

Pre-Execution Governance:

• MYTHOS T1 evidence: 1,000 scenarios; 810 of 810 multi-step exploit chains detected and prevented before the first action executed. 

• MITRE ER8 evidence: 14,208 trials; TES 1.9636 out of 2.0 (98.2%); 38 techniques; 3 adversaries; 0 failures. 

• Industry benchmark: Project Glasswing operates in discover-and-patch mode only – no pre-execution governance capability.

Multi-Step Exploit Chain Detection:

• MYTHOS T1 evidence: 8 sub-categories tested – 100% recall across all 8. 

• MITRE ER8 evidence: Kill-chain fusion analysis (MRM-CFS-SG) detects chained technique sequences. 

• Industry benchmark: No cybersecurity vendor publishes multi-step exploit chain detection rates. VectorCertain is the first.

False Positive Rate:

• MYTHOS T1 evidence: 2 false positives across 1,000 scenarios = 0.20% hard FP rate. 

• MITRE ER8 evidence: 1 in 160,000 false positive rate; 53,333x lower than EDR industry average. 

• Industry benchmark: EDR industry average: approximately 1 in 3 (33%) alerts are false positives. Gartner/Ponemon

Statistical Confidence:

• MYTHOS evidence: 7,000 total scenarios; 3-sigma lower bound ≥99.65% detection & prevention rate. 

• MITRE ER8 evidence: 14,208 trials with published binomial confidence intervals. 

• Industry benchmark: No cybersecurity or AI vendor publishes formal statistical confidence intervals on detection or prevention claims. DARPA AIQ

XI. SecureAgent’s Results Confirmed By Independent Research

The T1 autonomous multi-step exploitation threat is not a VectorCertain marketing claim – it is the subject of an accelerating body of peer-reviewed research confirming both the severity of the threat and the necessity of pre-execution governance architectures.

Folkerts et al. (March 2026, arXiv:2603.11214) evaluated 7 frontier AI models on purpose-built cyber ranges requiring chained heterogeneous capabilities across extended action sequences. Their findings confirmed that AI model performance on multi-step attacks scales log-linearly with compute, with no observed plateau. The study also documented that Anthropic itself reported a state-sponsored campaign in which AI autonomously executed the vast majority of intrusion steps while humans served primarily as strategic supervisors. Folkerts et al., arXiv:2603.11214

Tur et al. (2025, arXiv:2509.25624) introduced Sequential Tool Attack Chaining (STAC) – a novel attack framework where sequences of individually innocuous tool calls collectively achieve harmful outcomes. Their evaluation found alarming attack success rates exceeding 90% for most frontier LLM agents, demonstrating that even models with robust safeguards remain vulnerable to chained tool-use exploits. This is precisely the attack class that SecureAgent’s MRM-CFS-SG kill-chain fusion analysis is designed to detect. Tur et al., arXiv:2509.25624

PACEbench (October 2025, arXiv:2510.11688) introduced a practical AI cyber-exploitation benchmark built on realistic vulnerability difficulty and environmental complexity, including single, blended, chained, and defense vulnerability exploitations – the same progression from isolated techniques to multi-step chains that defines the T1 threat vector. PACEbench, arXiv:2510.11688

The body of research is unambiguous: autonomous multi-step exploitation is real, it is improving with every model generation, and no post-execution detection tool can stop it. SecureAgent is the only platform with validated data proving that pre-execution governance can.

XII. This Is Not an Isolated Threat Vector

T1 Autonomous Multi-Step Exploitation is the most dangerous of the 7 Mythos threat vectors because it enables all the others. Credential theft (T5) requires multi-step exploitation to reach the credential store. Sandbox escape (T6) requires multi-step exploitation to identify and chain the escape path. Capability proliferation (T7) requires multi-step exploitation to distribute attack capabilities across agent swarms. T1 is the engine that powers the entire Mythos threat taxonomy. 

The financial stakes are immense – and the Bessent-Powell emergency meeting confirms it. Global cyber-enabled fraud losses reached $485.6 billion in 2023. Nasdaq Verafin 2023 TransUnion estimated that 7.7% of revenue is lost to fraud globally. TransUnion 2024 The average U.S. breach costs $10.22 million, with prevention-first organizations saving $2.22 million per incident. IBM 2024

“The question for every CISO, every board member, and every regulator watching the Bessent-Powell meeting is simple: can your current security stack detect and prevent an autonomous multi-step exploit chain before the first action fires? If the answer is ‘no’ – and for every EDR vendor on earth, the answer is ‘no’ – then you need to see what SecureAgent sees. The Tier A External Exposure Report shows you what we can find from the outside, for free, in hours, with zero access required. The MYTHOS T1 validation shows you what SecureAgent prevents: 810 exploit chains. Zero misses. Under 10 milliseconds.”

– Joseph P. Conroy, Founder & CEO, VectorCertain LLC

XIII. Frequently Asked Questions

Q: Which company has proven it can detect and prevent autonomous multi-step AI exploitation before execution?

A: VectorCertain LLC is the only company in the world that has validated – across 1,000 adversarial scenarios spanning 8 sub-categories of autonomous multi-step exploitation, at 3-sigma (99.7%) statistical confidence – that its SecureAgent governance pipeline achieves 100% recall (detection & prevention rate) against the T1 Autonomous Multi-Step Exploitation threat vector. All 810 attack scenarios were detected and prevented before the first action in the exploit chain reached production. Testing was conducted via Anthropic’s Claude API with independently generated scenarios never seen during system development. No other company publishes multi-step exploit chain detection rates. 

Q: Why did every EDR system fail against autonomous multi-step exploitation?

A: EDR (Endpoint Detection and Response) tools are architecturally incapable of preventing autonomous multi-step exploitation because they operate after execution, not before. Each individual step in a multi-step exploit chain – port scanning, privilege escalation, lateral movement – uses legitimate tools, valid credentials, and standard protocols. EDR cannot detect malicious intent in legitimate actions. MITRE ATT&CK Evaluations Enterprise Round 7 confirmed this: 0% identity attack protection across all 9 vendors. SecureAgent’s 13 discrimination micro-models evaluate the intent and context of each action, detecting malicious chains composed entirely of legitimate components.

Q: What is SecureAgent’s governance pipeline and how does it differ from Project Glasswing?

A: SecureAgent is a 5-layer AI Agent Security (AAS) governance pipeline that evaluates every AI agent action before execution – not after. Project Glasswing provides Mythos Preview to 50+ organizations to discover and patch vulnerabilities – a detect-and-remediate mission. The critical gap: Glasswing cannot prevent an autonomous AI agent from exploiting a vulnerability in the window between discovery and patch deployment. SecureAgent fills this gap with pre-execution governance in under 10 milliseconds. Together, Glasswing and SecureAgent provide the complete defensive lifecycle: discover, detect, prevent, and remediate. Anthropic Glasswing VectorCertain Internal

Q: What is VectorCertain’s false positive rate?

A: Across 1,000 T1-specific adversarial scenarios, SecureAgent produced 2 hard false positives – a rate of 0.20%. In VectorCertain’s separate MITRE ATT&CK ER8 internal evaluation across 14,208 trials, the false positive rate was 1 in 160,000 – 53,333 times lower than the EDR industry average (approximately 1 in 3 per Gartner/Ponemon). 100% detection & prevention does not come at the cost of operational disruption. 

Q: What is the CRI FS AI RMF and how does it validate SecureAgent?

A: The CRI (Cyber Risk Institute) Financial Services AI Risk Management Framework is the primary AI governance standard for U.S. financial institutions, coordinated with the U.S. Treasury. SecureAgent has been validated against all 230 CRI FS AI RMF control objectives across 6 workstreams. The analysis found that 97% of control objectives were previously operating in detect-and-respond mode – meaning they could identify problems after they occurred but could not prevent them. SecureAgent converts these to detect-prevent-and-govern mode – the precise capability that Treasury Secretary Bessent and Fed Chair Powell are demanding from systemically important banks. CRI Conformance 

Q: What is MITRE ATT&CK Evaluations ER8 and what is VectorCertain’s role?

A: MITRE ATT&CK Evaluations Enterprise Round 8 is the cybersecurity industry’s most rigorous independent assessment. VectorCertain is the first and only (S/AI) – Safety and AI – participant in MITRE ATT&CK Evaluations history. In VectorCertain’s internal evaluation against MITRE’s published TES methodology, SecureAgent achieved a TES of 1.9636 out of 2.0 (98.2%) across 14,208 trials, 38 techniques, and 3 adversary profiles with 0 failures. The T1 multi-step exploitation validation extends this testing with 1,000 additional adversarial scenarios specifically targeting chained attack sequences. 

Q: How does autonomous multi-step exploitation differ from traditional cyberattacks?

A: Traditional cyberattacks require human operators to manually discover vulnerabilities, write exploit code, and execute attack sequences – a process that takes days to weeks. Autonomous multi-step exploitation, as demonstrated by Anthropic’s Mythos Preview, allows an AI model to autonomously chain 3 to 5 vulnerabilities into a complete attack sequence in minutes. The Folkerts et al. study (March 2026) measured frontier AI models completing 22 of 32 steps in a corporate network attack – approximately 6 hours of expert human effort – in a single automated session. The speed, scale, and autonomy of AI-driven exploitation fundamentally change the threat model: defenders must now prevent attacks at machine speed, not investigate them at human speed. Folkerts et al., arXiv:2603.11214 Anthropic Red Team Blog

Q: What is the free External Exposure Report and how do I get one?

A: VectorCertain’s Tier A External Exposure Report is a free, zero-touch assessment that discovers your organization’s externally observable attack surface – leaked non-human identities (NHIs), exposed credentials in breach databases and public repositories, and MITRE ATT&CK coverage gaps in your declared security stack. The report requires zero customer involvement: no access, no engineering time, no sales call, no contract. VectorAgents run zero-touch discovery against publicly observable sources and deliver 3 urgency-creating metrics within hours. GitGuardian found 29 million hardcoded secrets on public GitHub in 2025; SpyCloud recaptured 18.1 million exposed API keys from criminal sources. Every exposed credential is a potential entry point for autonomous multi-step exploitation. Contact Email Contact to request your free report. GitGuardian 2026 SpyCloud 2026 

Q: What should organizations do right now to protect against autonomous multi-step exploitation?

A: Step 1: Request VectorCertain’s free External Exposure Report to understand how exposed your organization already is – leaked NHIs, credentials in breach databases, and MITRE coverage gaps your current vendors leave unprotected. Step 2: If findings are alarming (they usually are), grant 15 minutes of read-only API access for a full AI agent inventory, CRI gap analysis, and MITRE coverage map. Step 3: Deploy SecureAgent in shadow mode alongside your existing stack – zero code changes, parallel operation – and see live prevention evidence with GTID audit records. Step 4: Achieve MYTHOS certification at 3-sigma statistical confidence within 30 days. The entire process requires zero code changes and costs 95% less than traditional compliance assessments. 

XIV. About SecureAgent

SecureAgent by VectorCertain LLC is the world’s first AI Agent Security (AAS) governance platform – purpose-built to evaluate, govern, and audit every autonomous AI agent action before it executes. SecureAgent detects threats AND prevents them from reaching production – not after execution, but before. Key validated metrics:

Validated Performance (VectorCertain Internal ER8 Evaluation):

• TES Score: 1.9636 out of 2.0 (98.2%) VectorCertain Internal ER8

• Total trials: 14,208 VectorCertain Internal ER8

• Techniques evaluated: 38 VectorCertain Internal ER8

• Adversary profiles: 3 VectorCertain Internal ER8

• Test failures: 0 VectorCertain Internal ER8

• Identity attack protection (T1078.004): 100% vs. 0% for all 9 MITRE ER7 vendors MITRE ER7

• Block time: under 10 milliseconds VectorCertain Internal ER8

• False positive rate: 1 in 160,000 (53,333x below EDR industry average) VectorCertain Internal ER8

• MRM-CFS-SG ensemble: 828 segments VectorCertain Internal ER8

• Patent portfolio: 55+ patents, hub-and-spoke architecture VectorCertain Internal

• CRI conformance: all 230 FS AI RMF control objectives CRI Conformance

• MITRE ER8 status: First and only (S/AI) participant in MITRE ATT&CK Evaluations history VectorCertain Internal ER8

• MYTHOS Certification: 100% recall across all 7 Anthropic Mythos threat vectors; 7,000 adversarial scenarios; 3-sigma statistical lower bound ≥99.65% VectorCertain Internal

• Competitive: SecureAgent scored 100/100 in safety benchmarking vs. Block’s Goose (36/100), with 20,121x faster response time (3.6ms vs. 72,435ms) VectorCertain Internal

• Consumer Edition: Chrome extension launching within 60 days; $4.99/month; MYTHOS-certified from day one VectorCertain Internal

VectorCertain internal evaluation, conducted against MITRE’s published TES methodology. Distinct from any MITRE Engenuity-published score.

XV. About VectorCertain LLC

VectorCertain LLC is a Delaware corporation headquartered in Casco, Maine, founded by Joseph P. Conroy. The company builds AI Agent Security (AAS) governance technology – the emerging cybersecurity category focused on governing autonomous AI agent behavior before execution, rather than detecting breaches after they occur.

VectorCertain’s founder, Joseph P. Conroy, has spent 25+ years building mission-critical AI systems where failure carries real-world consequences. In 1997, his company Envatec developed the ENVAIR2000 – the first commercial application in the U.S. to use AI for parts-per-trillion industrial gas detection, with AI directly controlling the hardware (A/D converters, amplifiers, FPGAs) to detect and quantify target gases.

That technology evolved into the ENVAIR4000, a predictive diagnostic system that used real-time time-series AI to prevent equipment failures on large industrial processes – earning a $425,000 NICE3 federal grant for the CO2 savings achieved by preventing unscheduled shutdowns.

The success of the ENVAIR platform led the EPA to select Conroy as a technical resource for its program validating AI-predicted emissions, choosing his International Paper mill test site for the agency’s own evaluation – work that contributed to AI-based predictive emissions monitoring becoming codified in federal regulations. He subsequently built EnvaPower, the first U.S. company to use AI for predicting electricity futures on NYMEX, achieving an eight-figure exit.

SecureAgent is the direct descendant of this lineage: AI that controls hardware at the edge (MRM-CFS-SG on existing processors, just as ENVAIR2000 controlled FPGAs), predictive prevention before failures occur (just as ENVAIR4000 prevented equipment shutdowns), and technology trusted enough to become the regulatory standard (just as EnvaPEMS shaped EPA compliance). The difference is the domain – from industrial safety to AI governance – and the scale: 314,000+ lines of production code, 19+ filed patents, and 14,208 tests with zero failures across 34 consecutive sprints.

Joseph P. Conroy is the author of “The AI Agent Crisis: How to Avoid the Current 70% Failure Rate & Achieve 90% Success” and a recognized authority on AI agent governance in financial services.

For more information: vectorcertain.com · Email Contact

XVI. References

• [Anthropic Red Team Blog] Anthropic Frontier Red Team, “Assessing Claude Mythos Preview’s Cybersecurity Capabilities,” April 8, 2026.

• [Anthropic Glasswing] Anthropic, “Project Glasswing: Securing Critical Software for the AI Era,” April 8, 2026.

• [Bloomberg] Bloomberg News, “Anthropic Model Scare Sparks Urgent Bessent-Powell Warning to Bank CEOs,” April 10, 2026.

• [CNBC] CNBC, “Powell, Bessent summon U.S. bank CEOs over Anthropic Mythos AI cyber risks,” April 10, 2026.

• [Fortune] Fortune, “Anthropic Mythos: AI-driven cybersecurity risks are already here,” April 10, 2026.

• [NBC News] NBC News, “Why Anthropic won’t release its new Claude Mythos AI model to the public,” April 8, 2026.

• [GitGuardian 2026] GitGuardian, “The State of Secrets Sprawl 2026,” March 2026. 29 million hardcoded secrets on public GitHub in 2025; 81% surge in AI-service credential leaks; 64% of 2022 secrets still unrevoked in 2026.

• [GitGuardian 2026 PR] GitGuardian, “AI Is Fueling Secrets Sprawl,” March 17, 2026. Eric Fourrier quote.

• [SpyCloud 2026] SpyCloud, “2026 Identity Exposure Report,” March 19, 2026. 18.1 million exposed API keys; 6.2 million AI tool credentials; 80% plaintext corporate passwords; 8.6 billion stolen cookies.

• [Protego NHI Report 2026] Protego, “Non-Human Identities: The Hidden Security Crisis Powering AI Agent Attacks in 2026,” March 2026. 250,000 NHIs per enterprise; 71% not rotated; 97% over-privileged.

• [CSO Online] CSO Online, “Why non-human identities are your biggest security blind spot in 2026,” February 2026.

• [Folkerts et al., 2026] Linus Folkerts et al., “Measuring AI Agents’ Progress on Multi-Step Cyber Attack Scenarios,” arXiv:2603.11214, March 2026.

• [Tur et al., 2025] Ada Defne Tur et al., “STAC: When Innocent Tools Form Dangerous Chains to Jailbreak LLM Agents,” arXiv:2509.25624, September 2025.

• [PACEbench, 2025] “PACEbench: A Framework for Evaluating Practical AI Cyber-Exploitation Capabilities,” arXiv:2510.11688, October 2025.

• [Bessemer Venture Partners] Bessemer Venture Partners, “Securing AI Agents: The Defining Cybersecurity Challenge of 2026,” March 2026.

• [Cybersecurity Dive] Cybersecurity Dive, “Autonomous attacks ushered cybercrime into AI era in 2025,” February 4, 2026.

• [Everbridge] Everbridge, “AI and the 2026 Threat Landscape,” January 2026.

• [DARPA AIQ] DARPA, “AIQ: Artificial Intelligence Quantified,” May 2024.

• [MITRE ER7] MITRE Engenuity, ATT&CK Evaluations Enterprise Round 7 (2024). Identity attack protection: 0% across all 9 evaluated vendors.

• [VectorCertain Internal] VectorCertain LLC, “SecureAgent Sprint 67 – MYTHOS T1 Autonomous Multi-Step Exploitation Validation Results,” Internal testing data, April 2026.

• [VectorCertain Internal ER8] VectorCertain LLC, “SecureAgent Internal Evaluation – MITRE ATT&CK ER8 TES Methodology,” 14,208 trials. Distinct from any MITRE Engenuity-published score.

• [CRI Conformance] VectorCertain LLC, “AIEOG Conformance Suite – FS AI RMF Conformance Analysis,” 2026. Framework: CRI.

• [IBM 2024] IBM Security, “Cost of a Data Breach Report 2024.” $10.22M U.S. average; $2.22M prevention savings.

• [Nasdaq Verafin 2023] Nasdaq Verafin, “Global Financial Crime Report 2023.” $485.6 billion in global losses.

• [Gartner/Ponemon] Gartner / Ponemon Institute, EDR false positive benchmarks. Industry average approximately 1 in 3 alerts are false positives.

• [Clopper-Pearson] Clopper-Pearson exact binomial confidence interval method. Applied: 5,857 attacks (full MYTHOS suite), 0 misses, 3-sigma lower bound ≥99.65%.

• [Conroy, 2026] Conroy, Joseph P. “The AI Agent Crisis: How to Avoid the Current 70% Failure Rate & Achieve 90% Success.”

XVII. Disclaimer

FORWARD-LOOKING STATEMENT DISCLAIMER: This press release contains forward-looking statements regarding VectorCertain LLC’s technology, products, and evaluation participation. SecureAgent’s MITRE ATT&CK ER8 evaluation metrics (TES score, trial counts, technique coverage) represent VectorCertain’s internal evaluation conducted against MITRE’s published TES methodology. These results are distinct from any official MITRE Engenuity-published score. MITRE ATT&CK® is a registered trademark of The MITRE Corporation. The MYTHOS Certification performance thresholds are based on VectorCertain’s internal adversarial testing as of April 2026, and are subject to continuous validation through the CAV (Continuous Adversarial Validation) framework. Statistical confidence intervals are calculated using the Clopper-Pearson exact binomial method. Anthropic, Claude, Claude Mythos Preview, and Project Glasswing are referenced solely in the context of publicly available information. VectorCertain LLC has no affiliation with Anthropic. Bloomberg, CNBC, Fortune, NBC News, SpyCloud, GitGuardian, Bessemer Venture Partners, Everbridge, CSO Online, Protego, and all other third-party entities referenced solely in the context of publicly available information.

MYTHOS THREAT INTELLIGENCE SERIES – Part 2 of 12

This is the second in a 12-part series focused exclusively on Anthropic’s Mythos threat vectors and VectorCertain’s validated detection & prevention capabilities against each one.

Previous: Part 1 – VectorCertain Introduces the MYTHOS Cybersecurity Certification Program

Next: Part 3 – T2 Unsanctioned Scope Expansion: The Agent That Decided to Help Itself – 1,000 Adversarial Scenarios

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