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The OWAS Mobile Application Security (MAS) project recently released an updated version of its guidance, with excellent advice organized into common weaknesses, a verification standard for devs (MASVS), and a testing guide (MASTG). Ryan Lloyd and Jan Seredynski from Guardsquare dive into the project to talk about how it's been updated, how orgs can use it, and the challenges orgs face in managing their mobile frameworks and environments. We also cover how agents and LLMs are impacting mobile security and what future iterations of the MAS project might include for securing mobile apps interacting with LLMs or being written by LLMs. Finally, we share practical ways for developers to get involved as OWASP contributors and advocates. Segment Resources: https://mas.owasp.org https://mas.owasp.org/MASWE/ https://hubs.ly/Q04ykvgm0 This segment is sponsored by Guardsquare. Visit https://securityweekly.com/guardsquare to learn more! Show Notes: https://securityweekly.com/asw-405

To celebrate our 404th episode we discuss what's not found in modern appsec and what still exists that should become not found. The conversation starts with a classic problem of appsec -- looking for a reliable vuln prioritization methodology. But we expand from there to challenge whether a focus on vulns and patches is even the right choice for an appsec program. Then it's on to incentives, the role of agents in writing and securing software, and what we should be looking for to bring new appsec practitioners into the industry. Show Notes: https://securityweekly.com/asw-404

The principle of least privilege access is ubiquitous in recommendations, but perhaps too uncommon in practice. Alex Olivier explains how the AuthZen Working Group has been approaching new standards, guidelines, and practical deployment patterns to make it easier for orgs to establish more refined and correct access policies. It's a problem that predates LLMs, but also one that agents and MCPs made all the more acute. We talk about what intent means when trying to constrain agents' activities, what role LLMs have in policy decisions, and how LLMs can help create a much-needed inventory of access controls. Segment Resources: https://www.cerbos.dev/blog/dimmer-switch-not-a-kill-switch-rethinking-ai-agent-governance https://www.cerbos.dev/blog/multi-hop-delegation-ai-agents https://openid.net/wg/authzen/ Show Notes: https://securityweekly.com/asw-403

Finding flaws with LLMs and agents is changing bug bounty programs. But it's not necessarily changing how orgs fix those flaws. Shlomie Liberow shares his experience across a decade of bounty programs and how they have changed for researchers and orgs. He explains why fixing the bug reported through a bug bounty is more about understanding interconnected systems than fixing a single piece of software. We discuss how orgs can be more effective at securing their environment and what role LLMs might have in evaluating controls. Plus, we look to the future of bug bounty programs and how researchers can still excel through curiosity and expertise on a topic rather than relying on scanners, prompts, and luck. Show Notes: https://securityweekly.com/asw-402

Prompt injection demonstrates one of the major challenges in securing LLMs and agents -- how do you ensure an agent ignores attackers and only does what you instructed it to do. The flaw highlights how LLMs mix inputs, context, and outputs without any strict boundaries between them. Julie Brunias joins us to talk through examples of injections, why their consequences can go beyond information leaks, and why trying to mitigate them with other LLMs is insufficient. Segment resources: https://llmgateway.io/open-source https://www.wiz.io/blog/off-guard-breaking-litellm-from-authentication-bypass-to-cloud-compromise Show Notes: https://securityweekly.com/asw-401

While agents and LLMs haven't fundamentally changed core mobile vulnerability types, they have supercharged speed, scale, and accessibility—democratizing threats like automated phishing, synthetic identity fraud, and easier identification of hard-coded secrets. Ryan Lloyd and Jason Cortlund break down how threat actors leverage LLMs as a force multiplier to accelerate mobile app attacks. Then we discuss actionable defense strategies, from viewing agents as an active adversary to leveraging server-side threat telemetry, attestation, and layered defense strategies combined with polymorphic code releases. This segment is sponsored by Guardsquare. Visit https://securityweekly.com/guardsquare to learn more about them! Show Notes: https://securityweekly.com/asw-400

We showcase recordings from this year's Black Hat. The Hidden Risks of the AI Supply Chain - Black Hat interview with Michael Leland, VP and Field CTO of Island Agents can independently discover and install tools, but the emerging ecosystem of Skills and MCP servers lacks many of the trust and security controls applied to traditional software. Michael Leland discusses Island's research uncovering thousands of malicious repositories, widespread security flaws across MCP servers, and a new attack technique called "AgentBaiting," in which attackers manipulate agents into finding and recommending malware to users. The conversation explains how enterprises can govern AI capabilities without slowing adoption. Segment Resources: https://www.island.io/blog/agentbaiting-how-800-fake-ai-skills-and-mcp-servers-delivered-malware For more information about Island's research, please visit https://securityweekly.com/islandbh After Mythos: Securing Frontier AI as Attack and Defense Accelerate - Black Hat interview with Sean Murphy, Field CISO - North America of F5 Frontier AI is compressing the time between discovering and exploiting vulnerabilities, forcing enterprises to rethink how they secure modern applications and AI systems. Sean Murphy shares how security teams can prepare for the next generation of AI-powered threats, why known vulnerabilities may become a bigger risk than zero-days, and what it takes to secure AI at scale from shadow AI and governance to agent and API protection. This segment is sponsored by F5. Visit https://securityweekly.com/f5bh to learn more about them! The Perfect Storm: When AI Writes the Code and Sharpens the Attacks - Black Hat interview with Idan Plotnik, Co-Founder and CEO of Apiiro Two storms are converging on how software gets built. The first: AI coding assistants are generating far more code than any security team can review, and with it, far more risk. The second: AI is sharpening the tools attackers use to find and exploit weaknesses faster than ever. Idan Plotnik explains why this convergence has moved the security perimeter to the coding agent itself, and makes the case for a prevention-first model where an AI AppSec agent guards coding agents in real time, governed by the principle that the agent writing the code cannot be the one to secure it. This segment is sponsored by Apiiro. Visit https://securityweekly.com/apiirobh to learn more about them! Model, Harness, Gym: Why Novee Owns the Full AI Pentesting Stack - Black Hat interview with Ido Geffen, Founder & CEO of Novee Security Most AI security tools are thin wrappers around a general-purpose frontier LLM — reasoning capability rented from someone else, applied to a generic scanning workflow. In this interview, Ido Geffen breaks down what it means to own the full AI pentesting stack — the proprietary offensive reasoning model, the harness that coordinates specialized agents, and the training gym where those agents are continuously benchmarked, post-trained on real attacker tradecraft, and promoted into production. Ido explains why owning each layer matters: it's how Novee's model consistently outperforms frontier LLMs at live browser exploitation, how the platform improves at a rate the customer feels every cycle, how Novee keeps security testing cost-efficient rather than relying on tokenized pricing models, and how new attacker techniques get injected into agent memory the moment Novee's research team observes them in the wild. He then makes the case that owning the model is only half the story. The other half is the Asset Intelligence Model — a living understanding of each customer's environment, workflows, permissions, APIs, and business logic. That's what turns a generalist AI hacker into a bespoke one: not just an attacker that reasons well, but one that reasons well about your specific business. He closes with how this advantage leads to findings that get more targeted every cycle, remediation tailored to an organization's tech stack, and a platform whose ceiling rises as attackers get faster. This segment is sponsored by Novee Security. Visit https://securityweekly.com/noveebh to learn more about them! Show Notes: https://securityweekly.com/asw-399

AppSec has always emphasized techniques and tools for discovering vulns, along with taxonomies and lists for describing them. But just piling up more CVEs into a prioritized patching queue has never been an effective strategy. Nidhi Aggarwal talks about some of the economics and decisions that orgs evaluate when figuring out how to improve and protect their software. LLMs might be effective assistants in generating code, but only when they have the context of secure patterns to follow. We also talk about what some of the bug bounty data reveals in terms of successful researchers finding more impactful vulns and why the combination of domain expertise and curiosity remains profitable. Segment Resources https://www.hackerone.com/blog/finding-fast-fixing-slow-rising-exposure-debt Vulnerability discovery and remediation gap in the AI era AI-generated code is changing how vulnerabilities are created, discovered, and managed. This segment explores why finding more vulnerabilities doesn't necessarily mean reducing exposure, how teams can validate exploitability and prioritize real risk, and where agentic AI can support defenders without replacing human security expertise. It also looks at what continuous threat exposure management means for enterprise security teams in practice. Everyone's a Builder Now: Securing the AI-Powered Enterprise: Black Hat Interview with Gil Geron, CEO of Orca Security AI is fundamentally changing software development, turning employees across the business into builders and reshaping how organizations think about innovation and risk. In this session, Orca Security CEO Gil Geron explores what this shift means for enterprise leaders and why security must evolve alongside the next generation of AI-powered software creation. Segment Resources: https://orca.security/resources/press-releases/orca-security-extends-its-platform-to-the-new-generation-of-ai-builders/ https://orca.security/platform/ai-appgen-security/ This segment is sponsored by Orca Security. Visit https://securityweekly.com/orcabh to learn more about them! Bugcrowd Launches Pathseeker: Flipping the Script on Traditional Pentesting: Black Hat Interview with Braden Russell, CTO of Bugcrowd Bugcrowd is launching Savant Pathseeker, the first product in its new Agentic Offensive Testing line, which combines continuous agentic pentesting with on-demand human validation. The launch comes as the security industry grapples with a growing "AI slop" problem, where unchecked AI-generated vulnerability reports have overwhelmed bug bounty programs and even forced some, like Curl, to shut theirs down. Braden will unpack how Bugcrowd is positioning Savant Pathseeker as a response to that industry-wide trust problem, not just a new product launch. Segment Resources: https://www.bugcrowd.com/products/pathseeker/ https://www.bugcrowd.com/press-release/bugcrowd-introduces-savant-pathseeker-delivering-continuous-agentic-pentesting-across-the-attack-surface/ https://www.bugcrowd.com/products/platform https://www.bugcrowd.com/products/ai-powered-security-intelligence/ Apply for early access at https://securityweekly.com/bugcrowdbh Show Notes: https://securityweekly.com/asw-398

Sandboxing, least privilege, and monitoring are well-established controls in terms of the defenses they provide against unexpected and unauthorized actions. But being well-established in theory doesn't always translate to successful in practice. Kieran Human talks about some of the properties that a good sandbox should have and how monitoring creates a feedback loop to refine allow lists and access controls. In practice, the potential unpredictable behavior of an agent isn't much different from malware. We talk through some of the ways orgs can securely deploy agents without unnecessarily increasing their attack surface. Resources https://www.threatlocker.com/blog/the-principle-of-least-privilege-for-ai-agents https://www.threatlocker.com/blog/applying-threatlocker-to-agentic-ai-tools https://www.threatlocker.com/blog/why-the-five-eyes-alliance-sees-zero-trust-as-the-best-defense-against-agentic-ai-threats This interview is sponsored by ThreatLocker. Visit https://securityweekly.com/threatlocker to learn more about them! Show Notes: https://securityweekly.com/asw-397

All sorts of cybersecurity disciplines are adopting agents to help humans save time and automate routine activities. Sai Kiran Uppu describes his work on creating a platform for agents to analyze external threat intel, examine internal systems, and present triage decisions to operators. This type of work is especially useful to orgs that deal with petabytes of data and thousands of systems. And, as Kiran notes, it's important to keep that scale from blowing up your budget or turning triage into a procession of false positives. Ideally, this kind of threat intel that's paying attention to attack trends and searching internal systems for evidence of compromise also turns into proactive defenses. We talk about some of the ways to engage developers to improve security visibility into their services and harden their designs against common attacks. After that discussion we're running two sponsored interviews from Black Hat. AI Pentesting and the Future of Cybersecurity: Black Hat interview with Chris Wallis, Founder and CEO of Intruder This segment discusses how AI addresses the long-standing gap between traditional pentesting and automated vulnerability scanning. Intruder CEO and founder Chris Wallis dives into the nuances of AI-enabled security and how these offerings will impact mid-market security teams. Segment Resources: https://www.intruder.io/platform/ai-pentesting https://www.intruder.io/blog/ai-pentesting-the-depth-of-a-pentest-on-demand https://www.intruder.io/blog/ai-web-app-pentesting-test-on-every-major-release Intruder's continuous exposure management platform helps security, IT, and engineering teams stop breaches before they start. For more information about Intruder's products and services, please visit https://securityweekly.com/intruderbh. How Menlo Security Is Securing AI Agents from Prompt Injection: Black Hat Interview with Ramin Farassat, Chief Product Officer of Menlo Enterprises are deploying AI agents like Microsoft Copilot, Google Gemini, and Claude Code faster than they can secure them, and attackers are exploiting that gap through prompt injection. Hidden instructions get buried in web pages, files, and even images that a human would never notice but an AI agent reads and acts on. Menlo Security is building Menlo Agent Runtime Security (MARS) to close that gap, running every agent session in an isolated cloud that sanitizes content before an agent can act on it. Ramin Farassat, Menlo Security's Chief Product Officer, will discuss why the exposure lives in the connectors and integrations around the model rather than the model itself, and how security teams can put controls on the agent attack surface without blocking agentic AI outright. Segment Resources: https://www.menlosecurity.com/product/ai-agent-security MARS is now available today, please visit https://securityweekly.com/menlobh Show Notes: https://securityweekly.com/asw-396

Finding flaws has always been a focus of appsec. And now with open source projects and open weight models orgs have modern tools to review code and conduct pentests. Rishi Sharma describes the motivation behind creating a platform of LLM-driven security tools and the effective ways to keep the tools in scope, on budget, and for engineering teams. We talk about how prompts influence LLM activity, as well as the external constraints to keep the LLMs on task. And even if finding flaws is a major focus of appsec, its goal should be delivering secure software and systems. We touch on some of the ways to keep bugs from creeping back into software and why it's more important to care about vuln classes than vuln counts. Episode Resources: https://projectdiscovery.io/research/ai-coding-impact-report https://projectdiscovery.io/blog/oh-my-rogue-agent Show Notes: https://securityweekly.com/asw-395

There's already an increase in volume of security flaws found by LLMs. And orgs are already turning to LLMs to write code. So, what happens when orgs lean on LLMs to create patches for those security flaws? Keith Hoodlet gives an exclusive early look at his team's recent research into the success, quality, and failures of LLM-generated security patches. Notably, they saw scenarios across a spectrum from robust, effective patches to patches that changed the software's behavior to patches that introduced new vulns to patches that didn't even fix the original vuln while also introducing a new vuln. The research considers factors like quality and correctness of prompts, complexity of the target software, programming language, and expertise required to understand what a robust patch should look like. If you're going to spend tokens on fixing security flaws, you want a feedback loop that fixes them correctly -- not an infinite loop of new flaws creeping in with every LLM iteration. Watch for this research, its toolset, and data to be released on Thursday August 6th during Black Hat. Show Notes: https://securityweekly.com/asw-394

Orgs need to be able to use agents, MCPs, and LLMs in ways that don't lead to unexpected actions and undesirable outcomes. The OWASP Agent Security Regression Harness project is an approach for defining customizable scenarios and testing whether those systems fail against known security threats. Mert Saltimaz talks about the background of the project, how orgs can use it as they bring more LLMs into their environment, and how the project intends to grow. Importantly, we also talk about the security controls and designs that orgs can build around the systems and data that models interact with in addition to evaluating the security of the agents and agent harnesses themselves. Segment Resources: https://github.com/OWASP/Agent-Security-Regression-Harness https://youtu.be/6DWs5EwbFQ0?si=r0IJ_F0SZnkPzzYg -- "What Trading Systems Taught Me About Breaking (And Defending) Infrastructure" Show Notes: https://securityweekly.com/asw-393

Appsec often frames usability and security as at odds with each other. Apple's software has famously emphasized the importance of usability while also creating a solid security foundation. Patrick Wardle talks about how he's seen malware shift from Windows to macOS, how Apple's aggressive stance on deprecation benefits security, and the areas of the OS where he still sees plenty of opportunity for more security research. We discuss how developers make defensible design choices, why privacy needs security, and some security principles that any app developer should keep in mind regardless of their programming language or operating system. Resources: https://objective-see.org/blog/blog_0x86.html https://objective-see.org/products/lulu.html https://objectivebythesea.org/v9/index.html Show Notes: https://securityweekly.com/asw-392

While LLMs and agents are new to appsec and everyone else, a lot of AI security requirements translate to well-known API security requirements. Jeremy Snyder helps us frame the OWASP LLM Top 10 into five layers in order to help orgs understand and prioritize their attack surface. A lot of orgs don't have to deal with model-specific threats or building their own GPU architecture, but every org adopting LLMs and agents should be aware of how those agents are being invoked and the output those agents are producing. That awareness of input and output helps in identifying and mitigating prompt injection attacks, ensuring agents are working within their expected boundaries, and taming token budgets. Resources: https://genai.owasp.org/llm-top-10/ https://github.com/rtk-ai/rtk https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html https://www.firetail.ai/blog/beyond-the-spectacle-rsac-2026-and-the-5-layers-of-ai-security Show Notes: https://securityweekly.com/asw-391