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Runtime SAST
Find and fix the vulnerabilities that actually run
Kodem makes static analysis smarter with AI use and adding runtime context. We use a blend of deterministic and LLM-based analysis to identify and confirm code vulnerabilities, then ground those findings in runtime evidence to show what actually runs, is reachable, and matters in production.
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“Kodem harnesses its unparalleled runtime expertise to release one of the strongest SAST offerings in the market. Finally, we can get real results, with virtually no false positives”
Runtime SAST, Explained
What is Runtime-Aware AI SAST?
Runtime-Aware AI SAST is a category of Static Application Security Testing that combines source code analysis with live runtime execution data. A traditional SAST tool scans your code for vulnerable patterns and flags every theoretical issue it finds. A runtime-aware AI SAST platform goes further: it confirms which of those flagged functions actually run in production, which inputs reach them, and which represent provable risk versus unreachable noise.
Kodem’s runtime-aware AI SAST platform supports modern compiled and scripted languages, persists runtime context across builds, and integrates into existing IDE and CI/CD workflows. The result is a SAST output that engineering teams can actually act on: a short list of vulnerabilities that are running, reachable, and worth fixing.
The Problem
Legacy SAST Tools floods teams with noise. Most of it never runs.
Traditional code scanning tools flag every potential weakness, even in dead or unreachable code. Without runtime awareness, teams waste time fixing issues that don’t matter while missing the ones that do.
The Solution
Kodem’s Runtime-aware AI SAST Platform connects static analysis to runtime execution.
We correlate vulnerable functions to real-world activity in your environment. Whether they were executed in production, which process loaded them, and how often. This is how you shift from "possible" to "provable" risk.
AI SAST With Runtime Grounding
Know which AI code paths actually executed
Kodem shows which model-touching functions, dataflow paths, and dependency calls were truly executed during inference and fine-tuning. You see proof-of-execution for risky flows (file I/O, network, deserialization, tool-use) so you can separate hypothetical model-side risks from real ones.
Runtime Correlation Across Stacks
Supports modern compiled and scripted languages
We use function traces, file open events, and symbol mapping to correlate runtime behavior across Java, Node.js, Python, Go, Rust, C++, and more.
Persistent Runtime Context
No signal lost between scans
Once a function is observed running, it stays flagged until resolved. You get continuity across builds and environments.
Exploitability-Aware Triage
Fix what runs, skip what doesn’t
We raise the priority of vulnerabilities confirmed in runtime so your team knows exactly what to tackle first.
Built for the Languages Your Engineering Team Ships
Kodem’s runtime AI SAST platform traces function-level execution across modern compiled and scripted languages. Each runtime is instrumented for symbol mapping, function call tracing, and dataflow correlation.
Java SAST
JVM-level function tracing with full call graph correlation across Spring, Quarkus, and Jakarta EE workloads.
JavaScript & TypeScript SAST
Runtime tracing for Node.js, Deno, and Bun. Symbol mapping across compiled TypeScript and source code.
Python SAST
Function-level tracing across Django, Flask, FastAPI, and AI/ML workloads. Coverage for both CPython and PyPy runtimes.
Go SAST
Goroutine-aware tracing with binary symbol resolution for compiled Go services in containerized environments.
Rust SAST
Compiled binary tracing with debug symbol correlation. Coverage for Tokio async runtimes and standard threaded workloads.
C/C++ SAST
Native binary instrumentation with DWARF symbol resolution. Function-level coverage across compiled C and C++ services.
Ruby SAST
Runtime tracing across Rails and Sinatra workloads. Method-level execution data with full stack trace correlation.
Scala SAST
JVM-level tracing for Akka, Play, and Spark workloads. Cross-language correlation for Scala and Java in the same service.
& more...
New language runtimes added regularly. Contact the Kodem team for current coverage of Kotlin, Swift, PHP, and others.
“Kodem’s runtime-aware AI SAST platform offers one of the strongest solutions available, delivering real-world results with virtually no false positives.”
"Kodem's platform offers one of the strongest solutions available, delivering real-world results with virtually no false positives."
Detect vulnerable functions actually executed in production
Correlate runtime-aware AI SAST findings with real application behavior
Maintain exploitability context across builds and environments
Get runtime-aware remediation suggestions
Runtime-powered SAST
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What to Know
Runtime-Aware AI SAST is a category of Static Application Security Testing that pairs source code analysis with live runtime execution data. Instead of flagging every theoretical vulnerability in your codebase, a Runtime-Aware AI SAST platform confirms which vulnerable functions actually execute in production. The result is a prioritized list of provable risks rather than thousands of findings most of which are unreachable.
Traditional SAST tools scan source code for patterns that look vulnerable. They produce findings without context, so engineering teams triage thousands of theoretical issues that may never run. Runtime-Aware AI SAST adds execution data: function-level traces, file open events, and symbol mapping. It confirms which flagged code is loaded, which functions are invoked, and which data paths are reachable in production. That filtering is what makes the output actionable.
Most SAST false positives come from flagged code that is dead, unreachable, or never invoked in production. Runtime-Aware AI SAST eliminates those by correlating each finding to actual execution data. If the function never runs and no input ever reaches it, the finding is filtered out. Kodem typically reduces SAST false positives by 90 percent or more.
Kodem’s Runtime-Aware AI SAST platform does not. It uses out-of-band runtime sensors that observe function-level execution without modifying application code, recompiling, or restarting services. This is the operational difference from RASP-style approaches that require in-app instrumentation.
Kodem supports Java (Spring, Quarkus, Jakarta EE), JavaScript and TypeScript (Node.js, Deno, Bun), Python (Django, Flask, FastAPI, AI/ML workloads, CPython, PyPy), Go (containerized services), Rust (Tokio, threaded workloads), C and C++ (DWARF symbol resolution), Ruby (Rails, Sinatra), and Scala (Akka, Play, Spark). New runtimes are added regularly.
Yes. Once a function is observed running in production, Kodem maintains that runtime context across subsequent scans, builds, and deployments. Findings remain flagged until they are resolved, so signal does not get lost between releases.
Legacy SAST tools rank findings by severity scores and pattern matches, with no awareness of whether the code actually runs. Kodem ranks findings by runtime exploitability: is the function loaded, is it executed, is the call path reachable from user input, and is the exposure present in this specific environment? That context-aware model is what cuts noise and surfaces the small set of vulnerabilities engineering should fix first.
