Introduction
Large Language Models (LLMs) are redefining how enterprises build applications, but they also introduce new, unique security risks. Recognizing this, the OWASP Foundation (the global authority on software security) released the OWASP Top 10 for LLM Applications, highlighting the most critical vulnerabilities in AI-powered systems.
At CyberDudeBivash, we bring you a deep-dive, engineering-grade technical breakdown of these risks, how attackers exploit them, and how defenders can secure LLM-powered apps.
OWASP Top 10 for LLMs — Technical Overview
1. LLM01: Prompt Injection
Attackers manipulate model instructions (via user input, docs, or hidden text) to override original intent.
- Impact: Data exfiltration, jailbreaks, malicious command execution.
- Mitigation: Input sanitization, contextual sandboxing, retrieval filters.
2. LLM02: Data Leakage
Models may expose sensitive or proprietary data in responses.
- Impact: API keys, PII, training data leaks.
- Mitigation: Secrets scanning, restricted context injection, redaction layers.
3. LLM03: Supply Chain Vulnerabilities
Use of unverified models, datasets, or dependencies can introduce malicious payloads.
- Impact: Trojaned models, poisoned training sets.
- Mitigation: Verify supply chain integrity, use signed model artifacts.
4. LLM04: Model Denial of Service (DoS)
Attackers craft resource-intensive prompts to spike computation costs.
- Impact: Service outages, cost blowouts, GPU exhaustion.
- Mitigation: Token & output caps, concurrency limits, anomaly detection.
5. LLM05: Insecure Output Handling
Downstream systems execute or trust model outputs without validation.
- Impact: Remote code execution, SQLi, XSS.
- Mitigation: Always sanitize/validate LLM outputs before execution.
6. LLM06: Training Data Poisoning
Attackers inject malicious samples into model training data.
- Impact: Backdoored models, targeted biases.
- Mitigation: Curated datasets, adversarial testing, data provenance checks.
7. LLM07: Insecure Plugin / Tool Use
LLMs integrating external tools (browsers, shells, APIs) may be tricked into misuse.
- Impact: Unintended system commands, data theft.
- Mitigation: Sandbox plugins, enforce strict API access controls.
8. LLM08: Excessive Agency
Over-empowering LLM agents to make autonomous decisions can cause damage.
- Impact: Unintended financial transactions, privilege misuse.
- Mitigation: Limit scope, human-in-the-loop approvals.
9. LLM09: Over-Reliance
Blind trust in LLMs can lead to flawed business logic and security gaps.
- Impact: False confidence, regulatory non-compliance.
- Mitigation: Verification pipelines, fallback deterministic logic.
10. LLM10: Model Theft
Adversaries steal or replicate proprietary models.
- Impact: Intellectual property theft, monetization loss.
- Mitigation: Watermarking, encrypted inference, secure deployment environments.
CyberDudeBivash Defense Framework
- Layered Guardrails: Token caps, tool limits, recursion breakers.
- Secure DevSecOps: Integrate LLM testing into CI/CD pipelines.
- Continuous Monitoring: Track anomalies in cost, latency, and prompts.
- Adopt Zero Trust for AI: Treat LLMs as untrusted components until validated.
- Leverage Security Tools:
CyberDudeBivash Analysis
The OWASP Top 10 for LLMs is not just a checklist—it’s a call to action. LLM-powered systems blur the lines between traditional app sec and AI security. Attackers exploit the creativity and adaptability of AI itself.
Enterprises must move beyond patching—towards proactive adversarial testing, red-teaming, and automated guardrails.
Final Thoughts
AI is a double-edged sword—unlocking innovation while opening new attack surfaces. CVEs in cloud, Kubernetes, and AI ecosystems (like CVE-2025-38500, CVE-2025-54914) prove that adversaries are already targeting the LLM stack.
At CyberDudeBivash, we deliver ruthless, engineering-grade threat intelligence to help you not only keep up—but stay ahead.
Ecosystem:
- cyberdudebivash.com
- cyberbivash.blogspot.com
- cryptobivash.code.blog
Business inquiries: iambivash@cyberdudebivash.com
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