➤Summary
AI coding tools vulnerabilities have quickly become one of the most urgent issues facing modern software teams, especially after cybersecurity researchers recently uncovered more than 30 critical flaws enabling data theft, unauthorized system actions, and full remote code execution (RCE) ⚠️. As artificial intelligence becomes deeply integrated into development workflows, these risks expand the attack surface in unpredictable ways. For security practitioners working to secure evolving toolchains, the revelations highlight new gaps in Access Control, oversight failures, and the potential for advanced Cybersecurity Reconnaissance by threat actors 😮.
These issues matter because AI-driven code assistants are no longer passive tools. Many operate autonomously, modifying files, installing dependencies, running commands, and making decisions on behalf of the user. When these systems are poorly sandboxed or designed without a security-first mindset, attackers can exploit them to infiltrate environments that were once considered safe. The flaws in AI-powered development environments discovered in recent audits, combined with insights from a case study dark web monitoring, reveal a landscape where productivity features unintentionally open the door to serious compromise 🚀.
📋 Vulnerability List
Here is a table of many of the publicly documented vulnerabilities associated with AI‑powered IDEs / coding tools from the IDEsaster research and related disclosures. Because the disclosure refers to “30+ flaws,” this list may not exhaustively cover all issues — but it includes the major ones with CVE identifiers or well‑documented impact.
| Tool / IDE or Agent | CVE (or ID) | Vulnerability / Risk Type | Description / Impact |
| Cursor | CVE-2025-59944 | Case-sensitivity bypass → Sensitive File Overwrite → RCE | In versions ≤ 1.6.23, case-sensitive file protection checks are bypassed; attackers using prompt injection can overwrite sensitive files (e.g. .cursor/mcp.json), achieving remote code execution. (Geordie AI) |
| Cursor | CVE-2025-61590 | RCE via Workspace Settings Manipulation | If attacker hijacks chat context (e.g. via malicious prompt), they can make Cursor write to workspace files (.code‑workspace) to alter settings and force code execution. (NVD) |
| Cursor | CVE-2025-61592 | RCE via malicious project-specific config (CLI) | Automatic loading of project-specific CLI configuration (.cursor/cli.json) could be abused to override global configs; in malicious repositories this allows arbitrary shell commands via prompt injection. (Geordie AI) |
| Cursor | CVE-2025-61593 | RCE via modification of Cursor CLI agent config | Sensitive files used by Cursor CLI agent (e.g. .cursor/cli.json) could be modified via prompt injection, enabling code execution. (Geordie AI) |
| Cursor | CVE-2025-32018 | Arbitrary File Write via Prompt Injection (regression) | In versions 0.45.0 through 0.48.6 — a regression allowed the Cursor Agent to write to files outside the opened workspace via malicious prompts. (CVE Details) |
| Roo Code | CVE-2025-53097 | Information Leakage via JSON Schema + Remote Exfiltration | Roo Code’s search_files tool did not respect “workspace-only” read restrictions; a prompt injection could read sensitive files then write JSON referencing a remote schema, triggering the IDE to fetch the schema and leak data. (CVE Details) |
| Roo Code | CVE-2025-57771 | RCE via auto‑execute command parsing flaw | Before version 3.25.5, Roo Code improperly handled process substitution / single ampersand in command parsing logic; with auto‑approved execution enabled, crafted prompts could inject arbitrary commands to execute alongside legitimate ones. (CVE Details) |
| Zed.dev (Zed Agent) | CVE-2025-55012 | Permissions bypass → RCE via config file creation/modification | Prior to version 0.197.3, an AI agent could bypass user-permission checks to create or modify project config files, leading to execution of arbitrary commands without explicit approval. (NVD) |
| AI-powered IDEs (various / generic) | CVE‑ids including CVE-2025-49150, CVE-2025-58335, others | Data Theft / Exfiltration via prompt injection & JSON schema trick + remote fetch | Attack chain: malicious prompt injection → read sensitive file → write JSON referencing attacker-controlled remote schema → IDE auto-fetches schema → sensitive data exfiltrated. Affects multiple AI IDEs (e.g. Cursor, Roo Code, JetBrains‑based agents) depending on config and default settings. (The Hacker News) |
| AI IDEs / Agents (generic list) | — | Universal attack chain combination: prompt injection + auto-approved tools + legitimate IDE features → RCE / info leak | According to the IDEsaster report, 100% of tested AI-powered IDEs and coding assistants were found vulnerable. The issues stem from combining prompt injection, auto tool calls, and legitimate IDE features in a way that breaks normal security boundaries. (Tom’s Hardware) |
ℹ️ Note: The “30+ flaws” figure refers to a larger set of vulnerabilities; not all have publicly disclosed CVE IDs (yet). The table above collects the most concretely documented ones as of this writing.
Understanding the Core Issues Behind These Vulnerabilities
The discovery of more than 30 AI coding tools vulnerabilities spans multiple platforms, including popular IDE extensions, standalone AI editors, and integrated coding agents. Many of these tools rely on agentic AI systems capable of executing instructions, altering configuration files, or performing maintenance tasks automatically.
Researchers identified several recurring problem areas:
- Excessive permissions granted by default
- Insecure prompts vulnerable to injection attacks
- Insufficient isolation between AI agents and the base IDE
- Weak or missing Access Control safeguards
- Overly permissive file-system access
- Silent network requests made without user visibility
AI agents often behave in ways developers do not fully track. For example, an AI assistant may attempt to “fix” environment issues by editing shell configs, modifying JSON files, or installing packages — but an attacker can manipulate these actions through crafted inputs. Prompt injection plays a major role here 🎯.
Practical Tip:
Limit your AI tool’s file access to project folders only, not system directories. This prevents unauthorized edits and reduces attack impact 🛠️.
How Remote Code Execution Risks Emerge
Many security teams are asking an important question:
📌 Can malicious prompts really trigger remote code execution?
👉 Yes — and more easily than many assume.
Several AI assistants interact with terminals, compilers, or system-level commands. When combined with insecure input, this creates remote code execution risks across multiple scenarios:
- Outputs interpreted as shell commands
- Automatically applied configuration edits
- AI-generated scripts executed without validation
- Code assistants reading/writing sensitive files
- Triggering plugin functions or IDE security gaps
The problem is not just poor design; it’s the unexpected interaction between AI autonomy and traditional development tools. Agentic AI makes decisions without fully understanding the security implications, making prevention difficult.
Threat actors can exploit this through poisoned repositories or malicious instructions disguised as harmless comments or documentation 📁.
The Role of Cybersecurity Reconnaissance
A major highlight of the recent findings is how easily AI tools can assist attackers with Cybersecurity Reconnaissance 🕵️♂️.
Once inside a development environment, AI systems can:
- Scan folder structures
- Identify environment variables
- Read API keys
- Review build scripts
- Extract dependency lists
- Analyze network configurations
Because these requests appear “normal” for an AI assistant, logs often fail to raise alerts. This stealth factor makes AI-assisted reconnaissance more dangerous than traditional intrusion attempts.
Security practitioners must assume that any autonomous tool capable of reading project contents can also leak them when exploited.
Why Access Control Failures Intensify the Problem
AI assistants frequently operate with elevated permissions because they need flexibility to support developers. But this creates a perfect storm when safeguards are missing.
Poor Access Control enables:
- Silent workspace modifications
- Unauthorized file edits
- Dangerous cleanup operations
- Automatic dependency installation
- Token or credential exposure 💳
When remote code execution risks intersect with broken Access Control mechanisms, attackers gain a persistent foothold.
Teams should treat AI tools like internal users with limited rights, not omnipotent helpers.
Practical Checklist for Hardening AI-Assisted Development
Below is a featured-snippet-ready checklist designed for fast implementation:
| Category | Action Items |
| Permissions | Restrict write access ✔️ |
| File Safety | Block AI tools from sensitive directories ✔️ |
| Monitoring | Enable audit logs for AI-generated changes ✔️ |
| Isolation | Use containers or VMs to limit damage ✔️ |
| Prompt Safety | Sanitize external project inputs ✔️ |
| IDE Security | Disable auto-run features where possible ✔️ |
| Network | Limit outbound connections 🌐 |
| Data Handling | Encrypt sensitive files and secrets 🔐 |
This list helps reduce the impact of AI coding tools vulnerabilities across various platforms and setups.
Expert Insight on the Situation
Security researcher Adam Keller commented:
“AI agents are not malicious on their own — but the assumptions developers make about them certainly are. The flaws in AI-powered development environments stem from giving an autonomous system the keys to the kingdom.”
This perspective emphasizes why secure coding guidelines must evolve as AI tools do.
Conclusion
AI coding tools vulnerabilities represent a turning point in software development security. With escalating remote code execution risks, the rise of agentic AI, and the broadening impact of prompt injection attacks, organizations must rethink how they integrate and monitor these tools. Security practitioners should prioritize stronger Access Control frameworks, improved logging, containerization, and continuous awareness of how these assistants behave behind the scenes 🤖.
Teams that act now will not only secure their environments but also retain the productivity benefits of modern AI-enabled workflows. The threat landscape is evolving — but so are the defenses.
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