What are some appsec basics? There's no monolithic appsec role. Broadly speaking, appsec tends to branch into engineering or compliance paths, each with different areas of focus despite having shared vocabularies and the (hopefully!) shared goal of protecting software, data, and users.

The better question is, "What do you want to secure?"

We discuss the Cybersecurity Skills Framework put together by the OpenSSF and the Linux Foundation and how you might prepare for one of its job families. The important basics aren't about memorizing lists or technical details, but demonstrating experience in working with technologies, understanding how they can fail, and being able to express concerns, recommendations, and curiosity about their security properties.

Resources:

• https://cybersecurityframework.io
• https://owasp.org/www-project-cheat-sheets/
• https://blog.cloudflare.com/rfc-8446-aka-tls-1-3/
• https://aflplus.plus/
• https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/

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Show Notes: https://securityweekly.com/asw-339

Appsec still deals with ancient vulns like SQL injection and XSS. And now LLMs are generating code along side humans. Sandy Carielli and Janet Worthington join us once again to discuss what all this new code means for appsec practices. On a positive note, the prevalence of those ancient vulns seems to be diminishing, but the rising use of LLMs is expanding a new (but not very different) attack surface. We look at where orgs are investing in appsec, who appsec teams are collaborating with, and whether we need security awareness training for LLMs.

Resources:

• https://www.forrester.com/blogs/application-security-2025-yes-ai-just-made-it-harder-to-do-this-right/

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Show Notes: https://securityweekly.com/asw-338

Manual secure code reviews can be tedious and time intensive if you're just going through checklists. There's plenty of room for linters and compilers and all the grep-like tools to find flaws. Louis Nyffenegger describes the steps of a successful code review process. It's a process that starts with understanding code, which can even benefit from an LLM assistant, and then applies that understanding to a search for developer patterns that lead to common mistakes like mishandling data, not enforcing a control flow, or not defending against unexpected application states. He explains how finding those kinds of more impactful bugs are rewarding for the reviewer and valuable to the code owner. It involves reading a lot of code, but Louis offers tips on how to keep notes, keep an app's context in mind, and keep code secure.

Segment Resources:

• https://pentesterlab.com/live-training/
• https://pentesterlab.com/appsecschool
• https://deepwiki.com
• https://daniel.haxx.se/blog/2025/05/29/decomplexification/

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Show Notes: https://securityweekly.com/asw-337

Fuzzing has been one of the most successful ways to improve software quality. And it demonstrates how improving software quality improves security. Artur Cygan shares his experience in building and applying fuzzers to barcode scanners, smart contracts, and just about any code you can imagine. We go through the useful relationship between unit tests and fuzzing coverage, nudging fuzzers into deeper code paths, and how LLMs can help guide a fuzzer into using better inputs for its testing.

Resources

• https://blog.trailofbits.com/2024/10/31/fuzzing-between-the-lines-in-popular-barcode-software/
• https://github.com/crytic/echidna
• https://github.com/crytic/medusa
• https://lcamtuf.blogspot.com/2014/11/pulling-jpegs-out-of-thin-air.html

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Show Notes: https://securityweekly.com/asw-336

What makes a threat modeling process effective? Do you need a long list of threat actors? Do you need a long list of terms? What about a short list like STRIDE? Has an effective process ever come out of a list? Farshad Abasi joins our discussion as we explain why the answer to most of those questions is No and describe the kinds of approaches that are more conducive to useful threat models.

Resources:

• https://www.eurekadevsecops.com/agile-devops-and-the-threat-modeling-disconnect-bridging-the-gap-with-developer-insights/
• https://www.threatmodelingmanifesto.org
• https://kellyshortridge.com/blog/posts/security-decision-trees-with-graphviz/

In the news, learning from outage postmortems, an EchoLeak image speaks a 1,000 words from Microsoft 365 Copilot, TokenBreak attack targets tokenizing techniques, Google's layered strategy against prompt injection looks like a lot like defending against XSS, learning about code security from CodeAuditor CTF, and more!

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Show Notes: https://securityweekly.com/asw-335

CISA has been championing Secure by Design principles. Many of the principles are universal, like adopting MFA and having opinionated defaults that reduce the need for hardening guides. Matthew Rogers talks about how the approach to Secure by Design has to be tailored for Operational Technology (OT) systems. These systems have strict requirements on safety and many of them rely on protocols that are four (or more!) decades old. He explains how the considerations in this space go far beyond just memory safety concerns.

Segment Resources:

• https://www.cisa.gov/sites/default/files/2025-01/joint-guide-secure-by-demand-priority-considerations-for-ot-owners-and-operators-508c_0.pdf
• https://www.youtube.com/watch?v=vHSXu1P4ZTo

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Show Notes: https://securityweekly.com/asw-334

The recent popularity of MCPs is surpassed only by the recent examples deficiencies of their secure design. The most obvious challenge is how MCPs, and many more general LLM use cases, have erased two decades of security principles behind separating code and data. We take a look at how developers are using LLMs to generate code and continue our search for where LLMs are providing value to appsec. We also consider what indicators we'd look for as signs of success. For example, are LLMs driving useful commits to overburdened open source developers? Are LLMs climbing the ranks of bug bounty platforms?

In the news, more examples of prompt injection techniques against LLM features in GitLab and GitHub, the value (and tradeoffs) in rewriting code, secure design lessons from a history of iOS exploitation, checking for all the ways to root, and NIST's approach to (maybe) measuring likely exploited vulns.

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Show Notes: https://securityweekly.com/asw-333

ArmorCode unveils Anya—the first agentic AI virtual security champion designed specifically for AppSec and product security teams. Anya brings together conversation and context to help AppSec, developers and security teams cut through the noise, prioritize risks, and make faster, smarter decisions across code, cloud, and infrastructure. Built into the ArmorCode ASPM Platform and backed by 25B findings, 285+ integrations, natural language intelligence, and role-aware insights, Anya turns complexity into clarity, helping teams scale securely and close the security skills gap.

Anya is now generally available and included as part of the ArmorCode ASPM Platform. Visit https://securityweekly.com/armorcodersac to request a demo!

As 'vibe coding", the practice of using AI tools with specialized coding LLMs to develop software, is making waves, what are the implications for security teams? How can this new way of developing applications be made secure? Or have the horses already left the stable? Segment Resources: https://www.backslash.security/press-releases/backslash-security-reveals-in-new-research-that-gpt-4-1-other-popular-llms-generate-insecure-code-unless-explicitly-prompted https://www.backslash.security/blog/vibe-securing-4-1-pillars-of-appsec-for-vibe-coding

This segment is sponsored by Backslash. Visit https://securityweekly.com/backslashrsac to learn more about them!

The rise of AI has largely mirrored the early days of open source software. With rapid adoption amongst developers who are trying to do more with less time, unmanaged open source AI presents serious risks to organizations. Brian Fox, CTO & Co-founder of Sonatype, will dive into the risks associated with open source AI and best practices to secure it.

Segment Resources: https://www.sonatype.com/solutions/open-source-ai https://www.sonatype.com/blog/beyond-open-vs.-closed-understanding-the-spectrum-of-ai-transparency https://www.sonatype.com/resources/whitepapers/modern-development-in-ai-era

This segment is sponsored by Sonatype. Visit https://securityweekly.com/sonatypersac to learn more about Sonatype's AI SCA solutions!

The surge in AI agents is creating a vast new cyber attack surface with Non-Human Identities (NHIs) becoming a prime target. This segment will explore how SandboxAQ's AQtive Guard Discover platform addresses this challenge by providing real-time vulnerability detection and mitigation for NHIs and cryptographic assets. We'll discuss the platform's AI-driven approach to inventory, threat detection, and automated remediation, and its crucial role in helping enterprises secure their AI-driven future.

To take control of your NHI security and proactively address the escalating threats posed by AI agents, visit https://securityweekly.com/sandboxaqrsac to schedule an early deployment and risk assessment.

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Show Notes: https://securityweekly.com/asw-332