Reware AI: Understanding the Landscape of Application Security

Reware AI: Understanding the Landscape of Application Security

By Reza Hazhirpasand May 28, 2025
Application SecuritySecurityTesting

At Reware AI, we’re on a mission to redefine how businesses approach software security. As we embark on this exciting journey, one of our foundational steps has been to rigorously test and understand the landscape of existing security tools. In our rigorous testings, we’ve come to realize that traditional software security tools, particularly Static Application Security Testing (SAST) and Dynamic Application Security Testing (DAST), often struggle with a significant challenge: their inherent limitations leave many critical vulnerabilities lurking in the blind spots of the codebase.

The Gaps in Traditional Security Testing

Static Application Security Testing (SAST) tools analyze source code without execution, aiming to “shift left” security. However, they’re often criticized for a high volume of false positives, leading to “alert fatigue” and wasted developer time (Wadhams et al., 2024; Ami et al., 2024). Studies show their effectiveness in detecting real-world vulnerabilities can be alarmingly low, with many flaws remaining undetected (Sen Chen, 2023; Ibrahim et al., 2024). SAST frequently misses vulnerabilities stemming from complex business logic, multi-component interactions, or runtime conditions, as they lack contextual awareness of how an application operates in a live environment (Aptori, 2023; Alkhadra et al., 2024). This includes issues like missing authorization checks, insecure deserialization, or race conditions, which require understanding runtime behavior (Al-Mutairi ets al., 2019; Ammar & Elrowayati, 2023). Such omissions result in dangerous false negatives – actual vulnerabilities that go unnoticed, posing significant risk.

Consider this Python Flask snippet demonstrating an Insecure Direct Object Reference (IDOR), a common logic flaw SAST often overlooks:

from flask import Flask, request, jsonify, session

app = Flask(__name__)
# Some necessary code

user_data = {
    "user123": {"name": "Alice", "balance": 1000},
    "user456": {"name": "Bob", "balance": 500}
}

@app.route('/get_balance/<user_id>')
def get_balance(user_id):
    # Assume session['logged_in_user_id'] is set after successful login
    # SAST tools typically struggle here. They might see 'user_id' used,
    # but they lack the runtime context to understand the *missing* authorization check
    # that 'user_id' must match 'session['logged_in_user_id']'.
    # A SAST tool primarily checks for direct input use in dangerous sinks (SQL, XSS),
    # not the *absence* of a crucial access control validation.
    if user_id in user_data:
        return jsonify(user_data[user_id])
    return jsonify({"error": "User not found"}), 404

if __name__ == '__main__':
    app.run(debug=True)

In the get_balance function, a SAST tool would likely not flag the absence of a check ensuring that the user_id requested matches the ID of the currently logged-in user (session['logged_in_user_id']). This authorization flaw, where one user can access another’s data simply by changing a parameter in the URL, is a business logic vulnerability that static analysis often fails to grasp without understanding the application’s runtime state and intended access policies.

Dynamic Application Security Testing (DAST), on the other hand, approaches security from an attacker’s perspective, interacting with the running application (Zou et al., 2019). Yet, DAST tools operate as a “black box” with no inherent knowledge of the application’s internal architecture, source code, or business logic. They primarily function by “throwing malformed data” at exposed interfaces, which can be inefficient and lacks depth. Even when configured with API schemas or login credentials, they provide only a superficial understanding, struggling to navigate complex user flows or identify deep, multi-step business process flaws (Ami et al., 2024; Ali et al., 2023). This limited visibility into application internals can also lead to incomplete coverage and a high rate of false negatives for certain types of vulnerabilities, particularly those not immediately discoverable through external interaction (Zou et al., 2019; Appknox, 2024). Although DAST interacts with the running application, its black-box nature can still cause it to fail to identify significant blind spots.

@app.route('/get_balance/<user_id>')
def get_balance(user_id):
    # Assume session['logged_in_user_id'] is set after successful login
    if user_id in user_data:
        return jsonify(user_data[user_id])
    return jsonify({"error": "User not found"}), 404

A DAST tool might attempt to access /get_balance/user456 while authenticated as user123. However, without inherent knowledge of the application’s internal business logic and defined user roles, the DAST tool may struggle to definitively identify that retrieving user456’s balance by user123 is an unauthorized action. It primarily observes HTTP responses. If the application doesn’t return an explicit 403 Forbidden or 401 Unauthorized in all unauthorized scenarios, and instead perhaps just an empty or generic error, the DAST tool might not infer the security implication. Its challenge lies in understanding the intended access control rules and differentiating legitimate from illegitimate behavior solely based on external interaction, particularly for subtle business logic flaws that aren’t tied to standard, easily detectable error codes or vulnerability patterns.

The Power of Human Expertise: Bug Bounty Programs

The most reliable method for uncovering impactful vulnerabilities, especially subtle business logic flaws or chained vulnerabilities that evade automated detection, often takes place within bug bounty programs. Platforms like HackerOne and Bugcrowd connect organizations with a global community of expert ethical hackers who thoroughly examine applications over days or even weeks (HackerOne, 2025; Bugcrowd, 2025). These security experts combine their deep understanding of various attack vectors with a keen eye for the application’s specific business flow and logic, often using a combination of manual techniques and various sophisticated tools (Intruder.io, 2025; LRQA, n.d.). It’s common for companies, including large technology firms, to utilize a robust security pipeline that includes both automated SAST/DAST tools for foundational coverage and continuous integration, alongside active bug bounty programs to catch the more elusive, high-impact vulnerabilities that represent true blind spots (CyberTalents, 2020; E-PROCEEDINGS UMP, n.d.).

Reware AI’s Vision: Utilizing AI for Deeper Insights

A new paradigm is emerging in the landscape of software security, promising to bridge the gap left by traditional tools and even augment human expertise: the application of Large Language Models (LLMs). Their surprising ability to comprehend, generate, and complete complex coding tasks, and even identifying subtle semantic nuances, is proving incredibly relevant for spotting vulnerabilities in code. Unlike traditional tools that rely on predefined rules or surface-level interactions, LLMs can learn from vast datasets, allowing them to detect novel or context-dependent flaws that are typically hard to catch. Reware AI is one of the first initiatives in this groundbreaking field. We are utilizing one powerful language model that is slightly fine-tuned with security-crafted data and synthetically generated vulnerability patterns for the precise spotting of security flaws. Our goal is to overcome the inherent limitations of traditional security tools, providing a more comprehensive and accurate approach to finding those critical vulnerabilities that often remain hidden.

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