The AI App That Skipped Security: A Deep Dive into Perplexity’s Vulnerable Codebase

The AI revolution has ushered in a new era of mobile applications driven by powerful large language models (LLMs). One standout in this space, Perplexity AI, has garnered widespread praise for its ability to deliver concise, source-based answers to user queries. But under its seamless user experience lies a worrying reality: deeply embedded security vulnerabilities that threaten the privacy, safety, and integrity of millions of users.

A recent comprehensive audit revealed that Perplexity’s Android app harbors at least 10 high-risk security flaws, many of which expose sensitive user data and application logic to potential attackers. These revelations serve as a sobering wake-up call not just for Perplexity, but for the entire AI-powered mobile ecosystem.

The Rise of Perplexity AI: Innovation Meets Explosive Adoption

Founded in 2022, Perplexity AI aimed to redefine information access through real-time, citation-rich answers using foundational models from OpenAI, Meta, and Anthropic. The platform's rapid growth is reflected in these numbers:

• 10+ million downloads on Google Play Store

• Monthly active users estimated at 15 million+

• Raised $165 million in its Series B round in January 2025

• Valuation exceeding $1 billion, with expectations of a future $18 billion public offering

Despite this success, the cybersecurity audit conducted by Appknox has revealed disturbing flaws that put the app’s future—and user trust—at serious risk.

Inside the Audit: Top 10 Security Vulnerabilities in Perplexity AI's Android App

Appknox used the OWASP Mobile Top 10 framework to identify the following security issues, categorized by type and severity.

Detailed Vulnerability Matrix

#	Vulnerability	OWASP Category	CVSS Score	Impact Summary
1	Hardcoded API Keys	M9: Reverse Engineering	9.8 Critical	Full access to backend APIs and user sessions
2	CORS Misconfiguration	M3: Insecure Communication	8.6 High	Cross-domain access by attackers
3	No SSL Certificate Pinning	M3: Insecure Communication	7.4 High	Allows MITM (Man-in-the-Middle) attacks
4	Bytecode Unobfuscated	M8: Code Tampering	7.1 High	Enables cloning and reverse engineering
5	StrandHogg 2.0 Susceptibility	M1: Improper Platform Use	8.1 High	Allows UI hijacking for credential theft
6	Root Detection Not Implemented	M2: Insecure Data Storage	6.9 Medium	Bypasses system restrictions
7	Outdated Network Configuration	M5: Insufficient Cryptography	6.5 Medium	Weak encryption of user traffic
8	ADB Debugging Not Disabled	M10: Extraneous Functionality	6.7 Medium	Allows code manipulation in emulators
9	Clickjacking Vulnerability	M4: Insecure Authentication	5.6 Medium	Trick users into tapping unintended UI elements
10	CVE-2017-13156 Exploit Possible	Legacy Exploit	6.3 Medium	App functionality hijack on older devices

“These vulnerabilities are not theoretical—many are trivial to exploit. For an app used by millions, this is a critical threat vector,”— Subho Halder, CTO & Co-founder, Appknox

The Broader Picture: Mobile AI Apps and Security Oversights

As AI becomes more consumer-facing through mobile apps, the consequences of poor cybersecurity hygiene are exponentially magnified. A 2024 report by Check Point Research shows:

• 43% of AI-related apps have misconfigured backend APIs

• 32% expose sensitive data like tokens, credentials, and user metadata

• 27% lack transport layer security (TLS) enforcement

AI Application Security Statistics (2024)

Metric	Global Avg. (AI Apps)	Perplexity AI
Backend API Protection	58% implement best practices	❌ Exposed APIs
SSL Certificate Pinning	46%	❌ Not Implemented
Code Obfuscation	71%	❌ Not Implemented
Regular Security Audits	52%	❓ Not Confirmed

Security Flaws in Context: Comparing Top AI Chat Apps

To understand the severity of Perplexity’s flaws, we benchmarked it against competitors like OpenAI’s ChatGPT and Google Gemini.

App	# of Known Vulnerabilities	Code Obfuscation	SSL Pinning	Root Detection
Perplexity AI	10	❌	❌	❌
ChatGPT (Android)	5	✅	✅	✅
Google Gemini	4	✅	✅	✅

“When mobile apps handle sensitive user data—especially from GenAI platforms—they must treat security as a design principle, not a patching strategy.”— Heather Adkins, VP of Security Engineering, Google

Real-World Threat Scenarios Enabled by These Flaws

1. MITM Attacks via Public Wi-Fi

   Without SSL pinning, attackers on a shared network can intercept user queries, impersonate API calls, and inject malicious responses.

2. Credential Theft via StrandHogg

   The app's lack of defense against this Android exploit allows attackers to create fake login screens and steal user credentials.

3. Reverse Engineering via APK Dumping

   With un-obfuscated code, threat actors can replicate the app, tamper with it, and distribute malware-laden clones.

Why AI Startups Fail on Security

• Speed Over Stability: Startups prioritize market launch over hardened infrastructure.

• Data Science Bias: Engineering teams are often built around ML talent, with little focus on mobile security.

• Lack of Threat Modeling: Many AI firms don’t conduct full-stack threat assessments pre-launch.

“Startups tend to believe that innovation comes first, and security can be bolted on. That belief will cost them their users—and their valuation.”— Alex Rice, Co-founder, HackerOne

Secure Deployment Guidelines for AI Chatbot Apps

Best Practice	Description
Token Rotation and Vault Storage	Avoid hardcoding credentials—use dynamic secrets
Implement SSL Certificate Pinning	Ensure only trusted servers can communicate with the app
Enable Root Detection Mechanisms	Prevent exploitation on compromised devices
Code Obfuscation and Anti-Tampering	Protect APK from reverse engineering
Deploy Continuous Vulnerability Scans	Integrate mobile security into CI/CD pipelines

“The intersection of AI and mobile is where the next generation of cyberattacks will emerge. If we don’t design securely now, we’ll pay heavily later.”— Dr. Niloofar Razi Howe, Board Member, Tenable

Toward Secure AI: What Must Change

The Perplexity incident is a symptom of a larger problem. AI products are rushing to market without proper security governance. The solution lies in redefining the AI development lifecycle:

• Integrate security audits in every sprint

• Involve mobile security engineers from MVP stage

• Include secure-by-design principles in app architecture

At the organizational level, founders and VCs must understand that security = valuation protection. A single exploit can wipe out years of growth.

Rebuilding Trust in AI Interfaces

Perplexity AI's exposure should serve as a clarion call across the tech industry. The innovation it represents is real—but so are the risks. As AI continues to embed itself in daily life, developers must adopt a mindset of proactive defense, not reactive response.

To learn more about how secure, scalable AI infrastructure can be implemented globally, explore expert insights by Dr. Shahid Masood and the research team at 1950.ai, pioneers in predictive AI, digital sovereignty, and cybersecurity.

Further Reading / External References

1. Perplexity AI’s Android App Security Audit – Forbes

2. 11 Bugs Found in Perplexity AI’s Android App – Dark Reading