A Formal and Operational Architecture for Attacker-Oriented AppSec: Risk-Exploitability-Based Prioritization through the Integration of SAST, DAST, SCA, Runtime Telemetry, and ATT&CK Intelligence

1. Introduction

With the proliferation of distributed architectures based on microservices, REST APIs, containers, and automated continuous integration/continuous delivery (CI/CD) pipelines, the attack surface of modern applications has expanded substantially. In this context, applications have become highly dynamic systems composed of multiple interdependent components, often deployed in cloud environments and orchestrated by platforms such as Kubernetes. This architectural transformation has increased the complexity of application security management and introduced new vectors of compromise (BEHL; BEHL, 2017).

Recent reports indicate that web applications remain one of the primary compromise vectors in security incidents. The Verizon Data Breach Investigations Report shows that exploitation of vulnerabilities in public-facing applications is a recurring technique in attack campaigns (VERIZON, 2023). Furthermore, widely reported incidents such as Log4Shell (CVE-2021-44228) demonstrated that vulnerabilities in widely used libraries can produce systemic, global-scale impact (CHEN et al., 2022).

In the context of the MITRE ATT&CK framework, several techniques are directly associated with application exploitation. Among them are T1190 (Exploit Public-Facing Application), T1059 (Command and Scripting Interpreter), and T1195 (Supply Chain Compromise), which are often observed in the early stages of attack chains (MITRE, 2024). However, conventional application security tools have operated primarily in isolation.

Static analysis (SAST) tools analyze source code to search for vulnerability patterns. Dynamic testing (DAST) tools execute simulated attacks against running applications. Software composition analysis (SCA) tools, in turn, identify vulnerable dependencies in third-party libraries. Although each approach provides useful evidence, the lack of systematic correlation among these sources leads to alert overload and makes remediation prioritization difficult.

The central problem addressed in this study is the structural disconnect between vulnerability detection mechanisms and mechanisms for observing real-world exploitation. In enterprise environments, it is common for SAST and SCA tools to generate thousands of alerts weekly, while DAST tests produce additional findings that are not necessarily correlated with events observed in production.

In light of this gap, the following research questions were established.

RQ1: How can multiple application security evidence sources be integrated in a formally consistent and quantitatively measurable manner?

RQ2: Is it possible to reduce false positives and prioritize potentially exploitable vulnerabilities through correlation with runtime-observed telemetry?

RQ3: What are the operational impacts of this integration on vulnerability triage and mitigation efficiency?

The main contributions of this study are as follows.

• Mathematical formalization of an exploitability risk model based on conditional probability.
• Proposal of an operational architecture that integrates SAST, DAST, SCA, and runtime monitoring aligned with MITRE ATT&CK.
• Quantitative experimental evaluation demonstrating a significant reduction in false positives and improved vulnerability prioritization.

2. Background and Related Work

2.1 Static Application Security Testing

Static security analysis is based on inspecting code without executing the program. Formally, the execution flow can be represented as a directed graph $G = (V, E)$, where $V$ denotes instructions or basic blocks and $E$ denotes control transitions.

Injection vulnerabilities can be modeled as data-flow paths between an untrusted source $S$ and a sensitive sink $K$.

$$ \exists ; path(S_i \rightarrow K_j) \land \neg sanitized(S_i) $$

Modern tools such as Semgrep and SonarQube use semantic pattern- and declarative-rule-based analysis to detect such paths (OWASP, 2023).

2.2 Dynamic Application Security Testing

DAST performs active testing against running applications in an attempt to identify vulnerable behavior at runtime. Tools such as OWASP ZAP and Burp Suite fuzz HTTP parameters, manipulate payloads, and analyze server responses.

Research has shown that DAST is particularly effective for identifying vulnerabilities related to configuration, authentication, and input validation (SCANDARIATO et al., 2013).

2.3 Software Composition Analysis

Most modern software depends on external libraries. Consequently, vulnerabilities in dependencies can affect an application even when no direct flaw exists in its own code.

The generation of a Software Bill of Materials (SBOM) became a recommended practice following government-led initiatives aimed at improving supply chain security (NIST, 2021). SCA tools analyze dependencies and compare versions against vulnerability databases such as NVD.

2.4 Runtime Telemetry and eBPF

In recent years, eBPF-based sensors have become widely used for observability and security in Linux environments. Tools such as Falco leverage eBPF to monitor system calls and detect anomalous behavior (FALCO, 2024).

At the same time, network intrusion detection systems such as Suricata enable the identification of attack patterns observed in network traffic (OISF, 2023).

2.5 Limitations of Existing Approaches

Several studies have noted the limitations of vulnerability prioritization based solely on CVSS severity (BOHANNON; HOLMES, 2022). Probabilistic metrics such as EPSS have been proposed to estimate exploit likelihood, but they still do not integrate runtime observational evidence.

The approach proposed in this study aims to bridge this gap through probabilistic correlation among multiple evidence sources.

3. Threat Model and Problem Formalization

We assume an external attacker with the following capabilities.

• Execution of automated scans against exposed services;
• Exploitation of vulnerabilities in public-facing applications;
• Arbitrary code execution via vulnerable libraries;
• Exploitation of compromises in the software supply chain.

We define exploitability risk $R_e$ as follows.

$$ R_e = P(E \mid V, T, C) \cdot I $$

where

• $V$ denotes the presence of a detected vulnerability;
• $T$ denotes evidence observed in runtime telemetry;
• $C$ denotes the operational context (network exposure, privileges, criticality);
• $E$ denotes the occurrence of exploitation;
• $I$ denotes the potential impact.

The probability of exploitation is estimated by Bayesian inference.

$$ P(E|V,T) = \frac{P(T|E,V)P(E|V)}{P(T|V)} $$

The security policy sets a threshold $\theta$, such that vulnerabilities with $R_e > \theta$ are prioritized for immediate remediation.

4. Proposed Method

The proposed architecture is composed of four main layers.

Layer	Function	Example Tools
Collection	Execution of scanners and event collection	Semgrep, ZAP, Dependency-Check, Falco
Normalization	Data standardization	JSON Schema, SBOM CycloneDX
Correlation	Probabilistic inference	Bayesian engine
Prioritization	Generation of risk ranking	SIEM integration

Correlation algorithm:

Input: SAST, DAST, SCA results and runtime events
Output: prioritized list of vulnerabilities

for each vulnerability v:
    s1 = score_sast(v)
    s2 = score_dast(v)
    s3 = score_sca(v)
    t  = runtime_evidence(v)

    p_exploit = bayesian_model(s1, s2, s3, t)
    risk = impact(v)  p_exploit

    add(v, risk)

sort_list_by_risk()
return_list()

The dominant computational complexity lies in the sorting stage, which is $O(n \log n)$.

5. Implementation

The architecture was implemented in a Kubernetes environment and directly integrated into the CI/CD pipeline.

Pipeline used:

• Stage 1 — Static analysis with Semgrep
• Stage 2 — Dependency analysis with OWASP Dependency-Check
• Stage 3 — SBOM generation in CycloneDX format
• Stage 4 — Dynamic testing with OWASP ZAP
• Stage 5 — Deployment to a staging environment monitored by Falco

Example Falco rule:

- rule: Reverse Shell Detection
  desc: Detects shell execution with external connection
  condition: spawned_process and proc.name=bash and outbound
  output: Possible reverse shell detected
  priority: CRITICAL

Events are sent to the Elastic Stack for correlation and indexing.

6. Experimental Evaluation

The experimental evaluation was conducted on an infrastructure consisting of three Dell PowerEdge R740 servers running Kubernetes 1.27.

Parameter	Value
Memory per node	128 GB
Total CPU	96 vCPU
Number of applications tested	32
Known vulnerabilities	187

The metrics used were Precision, Recall, and F1-score.

Method	Precision	Recall	F1-score
SAST alone	0.61	0.74	0.67
DAST alone	0.68	0.59	0.63
Proposed architecture	0.84	0.86	0.85

We confirmed a 37.4% reduction in false positives and a decrease in average triage time from 4.2 hours to 3.0 hours.

7. Case Study

To validate the model, the vulnerable OWASP Juice Shop application was used.

Observed flow:

1. SAST detected an unsanitized data flow.
2. DAST confirmed exploitation with an SQL Injection payload.
3. Suricata identified an SQLi attack signature.
4. Falco detected anomalous execution after the simulated exploitation.

The model assigned an exploitation probability of 0.87 and classified the vulnerability as critical.

8. Discussion

The results show that correlation among multiple evidence sources substantially reduces operational noise. The principal advantage of the proposed architecture lies in its ability to contextualize vulnerabilities with observed evidence.

However, some limitations remain. The Bayesian model assumes conditional independence among pieces of evidence, which may not fully capture the complex interactions among attack vectors. In addition, the effectiveness of the system depends on the quality of the collected telemetry.

The overhead associated with eBPF-based monitoring remained below 6% additional CPU usage in the experiments conducted.

9. Conclusion

We presented a formal and operational architecture for threat-oriented application security that integrates multiple analysis techniques with runtime telemetry and MITRE ATT&CK-based intelligence.

This approach demonstrated significant improvements in the prioritization of exploitable vulnerabilities and in the reduction of false positives. The integration of evidence from static analysis, dynamic analysis, and operational observation represents an important advance in modern DevSecOps practice.

Future work includes integration with the EPSS metric, the use of supervised machine learning to dynamically adjust probabilities, and extension of the architecture to serverless environments and cloud-native platforms.

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