The Boring Stuff is Dangerous Now TechTarget and Informa Tech’s Digital Business Combine.TechTarget and InformaTechTarget and Informa Tech’s Digital Business Combine.Together, we power an unparalleled network of 220+ online properties covering 10,000+ granular topics, serving an audience of 50+ million professionals with original, objective content from trusted sources. We help you gain critical insights and make more informed decisions across your business priorities.Dark Reading Resource LibraryBlack Hat NewsOmdia CybersecurityAdvertiseNewsletter Sign-UpNewsletter Sign-UpCybersecurity TopicsRelated TopicsApplication SecurityCybersecurity CareersCloud SecurityCyber RiskCyberattacks & Data BreachesCybersecurity AnalyticsCybersecurity OperationsData PrivacyEndpoint SecurityICS/OT SecurityIdentity & Access Mgmt SecurityInsider ThreatsIoTMobile SecurityPerimeterPhysical SecurityRemote WorkforceThreat IntelligenceVulnerabilities & ThreatsRecent in Cybersecurity TopicsApplication SecurityAttackers Weaponize RubyGems for Data Dead DropsAttackers Weaponize RubyGems for Data Dead DropsbyAlexander CulafiMay 13, 20264 Min ReadСloud SecurityLatAm Vibe Hackers Generate Custom Hacking Tools on the FlyLatAm Vibe Hackers Generate Custom Hacking Tools on the FlybyAlexander CulafiMay 13, 20265 Min ReadWorld Related TopicsDR GlobalMiddle East & AfricaAsia PacificLatin AmericaSee AllThe EdgeDR TechnologyEventsRelated TopicsUpcoming EventsPodcastsWebinarsSEE ALLResourcesRelated TopicsResource LibraryNewslettersPodcastsReportsVideosWebinarsWhite Papers Partner PerspectivesDark Reading Resource LibraryCyber RiskThreat IntelligenceVulnerabilities & ThreatsCommentaryThe Boring Stuff is Dangerous NowAI agents capable of discovering and exploiting obscure vulnerabilities are emerging alongside developers producing vast amounts of potentially flawed AI-generated code, forcing defenders to adapt accordingly.Shlomie Liberow,Founder and CEO,aisyMay 18, 20264 Min ReadSource: Yuri Arcurs via Alamy Stock PhotoOPINIONAre you freaking out? It feels like the entire industry is losing its head over the collision of two huge security pressures. First, every development team has suddenly been mandated to use AI coding tools, resulting in thousands of new bugs and misconfigurations. This has coincided with the announcement that, if Claude Mythos was unleashed, it would exploit every unknown vulnerability out there. It’s enough to make everyone from triagers and CISOs want to give up.Let’s consider how both scenarios play out, and what it means for vulnerability discovery, vulnerability management, and actual risk reduction.When Claude Code Security was announced earlier this year, there was a lot of hype around it being the silver bullet for insecure code. Cybersecurity stocks dropped, think pieces questioned if we’d all be out of a job. Enterprises were excited though by the massive improvements and possibilities offered by the models. In the past few weeks, mandates have swept through businesses, requiring all developers to use AI coding tools. Now, there’s no denying these tools are good, and the code they create is high quality and secure in itself. But that’s not where the security issues lie. It’s in the implementation where the risk sits; a broken assumption about how an API validates input or the same misconfigured permission pattern, repeated everywhere because developers are working fast and the feedback loop between "code shipped" and "vulnerability found" constantly shrinks. You’ve got a situation where developers are shipping at incredible speed, and CISOs are just expected to manage the risk. The question becomes: how can we build more security into the development and implementation process without putting more pressure on developers? Related:SecurityScorecard Snags Driftnet to Level Up Threat IntelligenceEnter Anthropic’s Project GlasswingPreviously, the implicit assumption in enterprise security was that obscurity offered partial protection. Attackers weren’t wasting their time on onerous discoveries. It took days of tedious recon to map a target's third-party ecosystem, such as which regional SaaS provider handles compliance, which internal tool has read access to production, or which open-source library sits six levels deep in the dependency tree. That friction acted like accidental insurance. Anthropic's Project Glasswing removes that barrier.Models like Mythos don't need creative genius, they just need reach. They have it, and that changes what counts as an attractive target. An agent can follow a trust graph systematically without fatigue and without distraction; the boring path through a forgotten vendor becomes highly exploitable, especially because nobody's watching it. Attackers don’t need a zero day when an agent can map your third-party ecosystem, identify which provider runs a known-vulnerable framework version, resolve the trust path to production, and chain it together. Related:Checkbox Assessments Aren't Fit to Measure RiskSo, we have this perfect storm of an explosion of new and poorly implemented code, with agents that can find the most obscure vulnerabilities, and chain them together to deliver maximum impact. What does this mean for organizations? Until now, they’ve been focused on locking down their most critical applications while legacy integrations and vendor tooling keep broad access quietly in the background. This is longer tenable. You have a situation where security teams are going to be more overwhelmed by vulnerability reports than ever. They essentially have the same problem — how do we know what to prioritize — just multiplied by a hundred. You can’t go to engineering teams with every reported vulnerability. You lose credibility if everything is urgent, when they don’t have time and patience to fix everything either. My advice to organizations is to start with focusing on what you’re most worried about. A critical vulnerability in a system that doesn’t hold any PII or provide privileged access isn’t as important as a combination of low-level vulnerabilities that result in actual high business impact. What do you need to protect against? Then go looking for everything that threatens it. If you start identifying common recurring themes, this intelligence can then be fed back into those AI coding tools so developers can be prompted at the moment of implementation that a common issue arises at this point and then mitigate for it. Overall, this reduces friction between security and engineering teams.Related:Research Hub Bridges Cybersecurity Gap for Under-Resourced OrganizationsThere are three things to consider when working out where your risk lies: Track transitive dependencies, data flows, permissions and the common patterns there. If you cannot answer "why does this keep happening?" quickly, you have a context gap.Prioritize patching the root causes based on the trust-path risk rather than asset prestige. The internal service nobody cares about can be higher risk than a flagship app if it sits on a more privileged path.Double down on remediating patterns of vulnerabilities. Over time, the focus should be enough pattern standardization that the AI tooling used to build learns from each mistake.This will help target the real risk and avoid overwhelming engineering at exactly the moment security teams need their trust. Don't miss the latest Dark Reading Confidential podcast, How the Story of a USB Penetration Test Went Viral. Two decades ago Dark Reading posted its first blockbuster piece — a column by a pen tester who sprinkled rigged thumb drives around a credit union parking lot and let curious employees do the rest. This episode looks back at the history-making piece with its author, Steve Stasiukonis. Listen now!Read more about:OpinionAbout the AuthorShlomie LiberowFounder and CEO, aisyShlomie is the founder and CEO of aisy. He spent nearly a decade as a hacker and head of Hacker R&D at HackerOne, working alongside Zoom, Salesforce, Capital One, and the Pentagon to find and triage thousands of vulnerabilities, making judgment calls on over $20 million in verified findings. Prior to this, Shlomie protected some of the world's most high-profile personalities from cyber threats, anticipating risks before they materialized and taking proactive measures to secure against them.See more from Shlomie LiberowWant more Dark Reading stories in your Google search results?Add Us NowMore InsightsIndustry ReportsHow Enterprises Are Developing Secure ApplicationsInside RSAC 2026: security leaders reveal the risks redefining your defense strategyHow Enterprises Are Harnessing Emerging Technologies in CybersecurityDitch the Data Center: Understanding Flexible Cloud Infrastructure Security Management2025 State of MalwareAccess More ResearchWebinarsHow Security Teams should apply Threat Intelligence into their DefensesYour Guide to Securing AI Adoption in Your OrganizationWhat is the Right Role for Identity Threat Detection and Response (ITDR) in Your Organization?The New Attack Surface: How Attackers Are Exploiting OAuth to Own Your Cloud WorkspacePrompt Injection Is Just the Start: Securing LLMs in AI SystemsMore WebinarsEditor's ChoiceThreat IntelligenceFrom Stuxnet to ChatGPT: 20 News Events That Shaped CyberFrom Stuxnet to ChatGPT: 20 News Events That Shaped CyberbyDark Reading Editorial TeamMay 6, 202631 Min ReadCyber RiskPhysical Cargo Theft Gets a Boost From CybercriminalsPhysical Cargo Theft Gets a Boost From CybercriminalsbyRobert LemosMay 4, 20265 Min ReadWant more Dark Reading stories in your Google search results?Keep up with the latest cybersecurity threats, newly discovered vulnerabilities, data breach information, and emerging trends. 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The convergence of artificial intelligence in software development and emerging capabilities in vulnerability discovery creates a new and dangerous security landscape, forcing a reevaluation of traditional vulnerability management and risk reduction strategies. This shift is driven by the simultaneous pressures of mandates requiring the use of AI coding tools, which introduce new bugs and misconfigurations, and the potential for sophisticated AI agents, such as Claude Mythos, to exploit previously unknown vulnerabilities. While there has been hype surrounding AI code security, the core security issues do not reside in the quality of the code itself, but rather in the implementation and configuration, where risks are often rooted in flawed assumptions regarding input validation or repeated misconfigurations across systems.

This dynamic creates a situation where developers operate at high speeds, and security teams are expected to manage risks that are compounded by a shrinking feedback loop between code deployment and vulnerability discovery. A significant evolution in threat vectors is the emergence of AI agents capable of systematically mapping an organization's entire third-party ecosystem, or trust graph. This capability bypasses the need for exhaustive, time-consuming reconnaissance by attackers, allowing them to follow trust paths to identify vulnerable open-source libraries, outdated framework versions, and necessary access rights within a complex vendor environment. This systemic mapping transforms what constitutes an attractive target, as exploiting a path through a forgotten vendor becomes highly exploitable without needing zero-day vulnerabilities.

The resulting "perfect storm" involves an explosion of newly written and potentially flawed code, the ability of agents to find and chain obscure vulnerabilities, and the resulting overwhelming volume of security reports. This inundates security teams, creating a challenge in prioritization: how to manage and remediate threats when they are multiplied by a hundred. The central challenge for organizations is shifting focus from merely locking down critical applications to addressing the deeper root causes of risk.

The suggested approach involves prioritizing risk based on trust-path analysis rather than asset prestige. Organizations should focus on tracking transitive dependencies, data flows, permissions, and recurring vulnerability patterns to identify context gaps where the operational reason for repeated issues cannot be quickly determined. Remediation efforts should focus on standardizing patterns of vulnerability and applying this knowledge to train the AI tooling used by developers. This allows the tools to learn from past mistakes, enabling them to proactively flag and mitigate common issues during the implementation phase. By focusing on identifying common, high-impact themes, organizations can feed this intelligence back into the AI coding tools, thereby reducing the friction between security and engineering teams and directing defensive efforts toward the most significant operational risks.