Application Security Testing

Android DAST is almost here. Here's what it took to build it. After a year of development, we're weeks away from releasing our Android Dynamic Application Security Testing (DAST) scanner. Why this matters: - Automatic exploit generation, for SAST findings too - 0% false positives - No root access required - No app configurations needed Here's how we solved the biggest challenges: 1. The migration challenge: Problem: 175+ existing vulnerability categories built for static analysis - do we start from scratch? Our approach: Built an abstraction layer so the same logic works for both static code and runtime checks. Result: 6 weeks to migrate instead of 6+ months to duplicate. 2. The performance challenge: Problem: With 175+ migrated security rules now running in dynamic mode, we faced a massive volume of runtime events. Most DAST tools use third-party frameworks like Frida, but these have limitations: require root access, struggle with the latest Android versions, and create performance bottlenecks. Our approach: Built our own solution using Java Debug Interface (JDI) with custom optimizations to filter only security-relevant method calls, avoiding the overhead of processing every single runtime event. Result: Transformed what would be unusably slow testing into sub-hour analysis without requiring root access or third-party dependencies. 3. The scale challenge: Problem: Cloud emulation creates massive slowdowns when testing ARM-based Android apps. Our approach: Purpose-built ARM-to-ARM cloud infrastructure. Result: Testing speed that rivals physical devices. The result → Android DAST analysis in under an hour with zero false positives and working exploits for every finding. While other tools tell you "this might be vulnerable," we show you exactly how an attacker would exploit it. P.S. Ready to be among the first to test our Android DAST? We're selecting several companies for early access in exchange for feedback. Drop a "+" below if you want to be considered.

Authorization vulnerabilities (IDOR, Vertical Privilege Escalation, Horizontal Privilege Escalation, Forced Browsing, etc.) are generally very impactful, but can be challenging for DAST scanners to find in a fully automated fashion. This is because traditional DAST scanners don't have any "context" of the business logic of applications and so they don't know what authorization checks to test for in the first place. And, since each application is unique, building a generic DAST scanner that can find authorization vulnerabilities at scale has been a very difficult problem to solve! Most struggle to even get the authenticated scanning working in heterogeneous environments. Forget about authorization. Some might argue that thats what API testing tools do. I agree but my counter to that point is that API testing tools can test for BOLAs or IDORs per API request, but they would not be able to chain multiple such authorization vulnerabilities to form a full kill chain of an escalation attack. For that, they'd have to know the threat model of the application being tested and I doubt there is any DAST scanner that takes in a threat model as an input to test for authorization vulnerabilities. But, I am happy to be proven wrong here! The coolest thing I've discovered over the past few days of using Claude Agent Skills with a DAST (Dynamic Application Security Testing) agent is that we can now harness the capabilities of a powerful coding agent (like Claude Code) to dynamically generate test scripts to test whether a vulnerability is exploitable or not against a live running application. In other words, it is possible to perform a live DAST test by creating test scripts on the fly depending upon the business logic of the application. These dynamically generated test scripts are based off of reference scripts that can be provided along with the SKILL definition and some examples to the agent. An example is provided in the comments below. This is going to be super useful to validate exploitability of vulnerability classes like Improper Authorization that are snowflakes (for lack of a better word). All this to say that - I believe we are at a point where we can provide a threat model of an application, along with its source code, sprinkled with some testing skills for specific vulnerability classes to a coding agent and we can have a web reasoning system (like the cyber reasoning systems built for AIxCC) that can discover, triage, validate and fix web application vulnerabilities in an autonomous manner. So far, I've been able to validate my hypothesis for the first 3 phases. Next up is the fixing phase and then finally the holy grail would be to bring it all together! If you're curious about how these skills could be built/leveraged, take a look at this "authorization-testing" skill that I built for the dast agent in SecureVibes leveraging Claude Agent Skills. Link in comments! #AppSec #Vibecoding #SecureVibes #OpenSource #BuildingInPublic #Agents

In this holiday break, I've spent time exploring LLMs and their potential application in application security testing. What started as curiosity evolved into an interesting project aimed at solving a persistent problem I've encountered throughout my career: the flood of false positives from traditional SAST tools. We've all been there - staring at security reports filled with generic warnings like "dangerous eval function" that lack context about actual exploitability. These tools flag issues without understanding execution paths or data flows, leaving teams drowning in false positives until they eventually start ignoring alerts altogether. In this project, I built a Multi-LLM Judge system that approaches security analysis differently: • Claude Code analyzes the entire codebase and traces data flows across components • DeepSeek and OpenAI models independently assess potential vulnerabilities • Claude API acts as judge to synthesize findings and eliminate false positives Instead of vague alerts, the system produces detailed tickets with exploitation paths, constructed HTTP requests for verification, and specific remediation steps. When tested against OWASP NodeGoat and Juice Shop, it identified sophisticated vulnerabilities while filtering out the noise. (You can adjust the judge scoring threshold for a balance in detail/noise). While this started as a personal project, I'm excited about the possibilities it opens up for more context-aware security testing approaches. For anyone interested in the technical details: Benchmarking and Results: https://lnkd.in/gh3N9HzM

Most product founders (or aspiring founders) think cybersecurity is something that can be added on as we go. In 2024, 68 % of breaches involved a non‑malicious human element, like misconfigurations or coding oversights. Security isn’t a checkbox at launch; it’s a mindset woven into every sprint, every pull request, every architectural decision. Here’s a playbook we, at GrayCyan, have developed: 1️⃣. Threat Model Upfront Before you write a single line of code, map out your attack surface. What data are you storing? Who could target it, and how? A lightweight threat model (even a few whiteboard sketches) helps you prioritize controls around your riskiest assets. 2️⃣. Secure Design Patterns Adopt proven patterns—like input validation, output encoding, and the principle of least privilege—right in your prototypes. Whether it’s microservices or monolithic apps, enforcing separation of concerns and privilege boundaries early means fewer surprises down the road. 3️⃣. Shift‑Left Testing Integrate static analysis (SAST), dependency scanning, and secret‑detection tools into your CI/CD pipeline. Automate these checks so that every pull request tells you if you’ve introduced a risky dependency or an insecure configuration—before it ever reaches production. 4️⃣. Continuous Code Reviews Encourage a culture of peer review focused on security. Build short checklists (e.g., avoid hard‑coded credentials, enforce secure defaults) and run them in review sessions. Rotate reviewers so everyone gets exposure to security pitfalls across the codebase. 5️⃣. Dynamic & Pen‑Test Cycles Complement static checks with dynamic application security testing (DAST) and periodic penetration tests. Even a quarterly or biannual pen‑test will surface issues you can’t catch with automated scans—like business‑logic flaws or subtle authentication gaps. 6️⃣. Educate & Empower Your Team Run regular “lunch‑and‑learn” workshops on topics like OWASP Top 10, secure cloud configurations, or incident response drills. When developers think like attackers, they write more resilient code—and spot risks early. 7️⃣. Plan for the Inevitable No system is 100 % immune. Build an incident response plan, practice it with tabletop exercises, and establish clear escalation paths. That way, when something does go wrong, you move from panic to precision—minimizing impact and restoring trust. At GrayCyan, we partner with founders (and upcoming founders that have amazing product ideas) to embed these practices as we build apps. If you’re ready to turn security from an afterthought into your competitive advantage, let’s connect. Drop a comment or send us a DM, and let’s bake trust into your next release. #DevSecOps #SecureByDesign #SecureDevelopment #DataProtection #TechStartups GrayCyan AI Consultants & Developers

One client experience that made me realize that not every pentest makes you "absolutely" secure. A fast-growing e-commerce startup invited us in after being hacked. They’d just done a penetration test with another vendor. The report looked professional, 40 pages of findings, but it turned out that the testers had only checked their staging environment. The live site had extra features, including an outdated API, that no one tested. That API became the attacker’s entry point. Lesson- Not all penetration testing is created equal. If you've got a pentest done recently, there are 7 warning signs it doesn’t truly protect you: • Vague results – You’re left guessing what the real issues are.  •No clear remediation steps – Your team doesn’t know where to start.  • Missed business logic vulnerabilities – Real-world attack paths unique to your app go undetected. Example-We once found a shopping cart that allowed discount codes to be applied multiple times by changing the request sequence. Scanners missed it because it wasn’t a code flaw - it was a broken business rule. • Outdated exploits & tools – Testing for yesterday’s threats, not today’s.  • Compliance-only focus – Enough to pass an audit, but not enough to keep you safe.  • No human validation – Automated scans without expert review.  • One-and-done delivery – No follow-up to confirm fixes worked. Pentesting should be more than ticking a box. It should combine speed, depth, and real human expertise so you get results you can act on fast. That’s why we’re building something new: a blend of automated security testing and expert-led VAPT, built to scale with your business and keep you safe from real-world threats. Tulsi Security #TulsiSecurity #ConvergeSouth #Cybersecurity #PenTesting #vapt

🚀Demystifying SAST, DAST, IAST & SCA: The Ultimate Application Security Cheat Sheet🔒   1. SAST (Static Application Security Testing)   - Definition: SAST is a *white-box* testing method that examines source code, bytecode, or binaries for vulnerabilities without executing the application. It analyzes the code from the "inside out," usually during the development phase. - Goal: To catch security flaws as early as possible by examining the code itself, such as SQL injection, cross-site scripting (XSS), and insecure de-serialization. - Example: A developer runs a SAST tool, such as SonarQube, on their source code for a web app. The tool flags a potential SQL injection vulnerability in the login code, helping the developer fix it before the code moves further into production.    2. DAST (Dynamic Application Security Testing)   - Definition: DAST is a *black-box* testing method that tests an application while it's running to identify vulnerabilities in a runtime environment, like a hacker would. - Goal: To find security vulnerabilities that occur only during runtime, like authentication issues, server misconfigurations, and business logic flaws. - Example: A security tester runs a DAST tool like OWASP ZAP against a live staging environment of a web app. The tool scans the application, finds an exposed admin page that lacks authentication, and reports it as a security risk.    3. IAST (Interactive Application Security Testing)   - Definition: IAST combines elements of both SAST and DAST. It works inside the application by instrumenting the code and monitoring the app's behavior during runtime. - Goal: To provide more in-depth, context-aware vulnerability detection by analyzing code as it executes, often integrated with automated testing during CI/CD pipelines. - Example: While running functional tests in a CI/CD pipeline, an IAST tool like Contrast Security identifies an insecure configuration vulnerability. This allows both development and security teams to get real-time alerts with contextual information to fix the issue efficiently.    4. SCA (Software Composition Analysis)   - Definition: SCA focuses on managing risks associated with third-party libraries and dependencies in an application by identifying and tracking open-source components. - Goal: To detect known vulnerabilities in third-party libraries or packages used in the project and ensure compliance with license requirements. - Example: A development team uses an SCA tool like Snyk on a Node.js project to scan its dependencies. The tool flags a critical vulnerability in a popular npm library, allowing the team to update to a secure version before releasing the product.   Each method has its strengths in detecting certain types of vulnerabilities, and together they provide comprehensive coverage for securing applications throughout their lifecycle. Check out the chart below for a full comparison.   #ApplicationSecurity #CyberSecurity #DevSecOps #SAST #DAST #IAST #SCA #AppSec #TechTips

Navigating AppSec tool jargon? I know Gartner and pals love their categories, but let's break them down into five key types 1.) SAST - Scans code for issues before it goes live. - It's usually meant for the code's creator to use. - if you want to scan open-source code or analyze malicious software for security purposes, they'd fall under a different category. 2.) DAST - Probes a live app from the outside, without needing access to the code. - It's a go to tool for pentesters to spot vulnerabilities under a blackbox testing strategy. 3.) IAST - Mostly run in dev environment to instrument and observe an app in action to understand its real-time behavior under a variety of test cases. - The agent has visibility into the source code and the application logs. 4.) SCA - Identifies the building blocks of an app, particularly open-source components, and checks for any known vulnerabilities. - It relies on databases like the NVD by National Institute of Standards and Technology (NIST) to highlight potential issues and often comes comes with a CVSS score. - determining actual risk requires a detailed analysis (like reachability analysis) and often CVSS scores need to be assessed in context of the application or network environment and business criticality 5.) ASPM - Tracks and manages our organization's app's security health over time. - It offers a continuous view of an app's security state, guiding efforts to bolster its defenses through ongoing monitoring and management. - gives you an overall visibility into your orgs application risk landscape - some vendors offer automatic remediation, false positive analysis To sum up, the essential five in app security are: 1.) SAST - - Snyk, Veracode, Checkmarx, Coverity ( now Synopsys Inc), Semgrep, GitHub ( github offers CodeQL), GitLab & Sudoviz ( use open source SAST) 2.) DAST - - Acunetix by Invicti, IntSights, a Rapid7 company, OWASP ZAP (open source), Checkmarx 3.) IAST - Contrast Security, Acunetix IAST, Checkmarx IAS, Fortify on Demand, Synopsys Inc Seeker 4.) SCA - - Snyk, Veracode, Checkmarx, Sudoviz, GitHub Dependabot, FOSSA, Sonatype, JFrog Xray, GitLab Dependency Scanning, OWASP Dependency-Check 5.) ASPM - Enso ( Snyk), ArmorCode Inc., Sudoviz ( Complete Posture management, code remediation, FP analysis), Cycode | Complete ASPM #appsec #sitereliability #sast #dast #sca #iac #ciso

Point-in-time penetration testing is officially a legacy model. Gartner just published two research papers that confirm exactly what we’ve been building toward: traditional assessments cannot keep pace with the velocity of modern, AI-augmented development. The future of #offensivesecurity is Continuous Offensive Security Testing (COST) - a trigger-driven, risk-aware model that activates when real application change happens. Novee Security was engineered specifically to deliver this model. By replacing manual, calendar-based assessments with autonomous #AI agents that explore the attack surface and probe for weaknesses in real-time, Novee ensures that exposure windows are closed the moment risk is introduced. The data from Gartner is clear. By 2028, over 60% of enterprise pen test programs will be embedded directly within #DevSecOps pipelines. Success will no longer be measured by a “test complete” checkbox, but by measurable reductions in exposure windows where findings drive immediate remediation. Novee’s proprietary #AIHacker mirrors elite human operators - discovering assets, generating attack hypotheses, and executing exploits - all without blocking CI/CD pipelines. Gartner named Novee in its sample vendor matrix for #PTaaS, and we believe that is just the starting point. Read more, here: https://lnkd.in/ghShP9GT

A data breach will cost you $4.88 Million, on average. Yet, most companies wait until production to find vulnerabilities. That single decision multiplies the cost of fixing them by 95x. Here's what elite companies do differently: Most software vulnerabilities are introduced during coding, but companies wait until production to find them. The math is brutal: • $80 to fix during design • $240 during development • $960 during testing • $7,600 in production Beyond the direct costs, companies face: • Emergency patches disrupting operations • System downtime killing revenue • Reputational damage from breaches • Legal and compliance nightmares • Lost productivity Your developers? They're spending 13 hours per week dealing with security issues. This creates a vicious cycle: delayed features, pushed back releases, and missed market opportunities. But elite companies, have cracked the code with DevSecOps - building security from day one. Google serves as a prime example of a large enterprise implementing advanced DevSecOps practices, particularly through Google Cloud Security Operations (SecOps) and integrated tooling. The results are staggering: • 15.4% lower breach costs • 50% faster time-to-market • 60% fewer security delays Here's their exact playbook: 1. Security as Code Treat security like regular code - version controlled, tested, and deployed alongside applications. Companies doing this see 28% better compliance rates. 2. Automated Security Testing Integrate security scanning directly into development. Use both static analysis and dynamic testing to catch vulnerabilities early. 3. Developer Security Training Organizations with comprehensive security training see 70% fewer incidents. It transforms developers into security-aware builders. The fascinating part? By "slowing down" to check security early, these companies ship features 50% faster. Why? They're not constantly firefighting security issues in production. After helping hundreds of companies secure their systems, I've noticed: The best companies don't react to threats. They prevent them. This is exactly why we built our security-first development process at Yellow Systems. We help CTOs and VPs of Engineering: • Catch vulnerabilities early • Implement automated security testing • Build robust, secure applications Want to see how secure your system really is? Visit yellow.systems for a comprehensive security audit of your entire stack. You'll get a detailed report of vulnerabilities and a clear roadmap for fixing them. We've helped 100+ companies build and maintain secure applications. Let's talk about yours.