Ameya Bhawsar
January 5, 2026
5 Minutes read
Proactive AI Security: Detecting Vulnerabilities Before They’re Exploited
As organizations deploy AI assistants that access sensitive internal data, new exploitation techniques are emerging. AI systems designed to support employees can be manipulated through crafted inputs to expose confidential information such as salary data or proprietary business details. These risks are no longer hypothetical and are being actively observed in real-world deployments.
AI applications introduce attack surfaces that traditional security controls are not designed to address. Unlike deterministic software, AI systems operate probabilistically, process unstructured natural language inputs, and rely on interconnected components such as embeddings, APIs, vector databases, and external tools. This architectural complexity creates unique opportunities for exploitation.
Why AI Security Testing Matters
AI systems, particularly Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) applications exhibit non-deterministic behavior and complex interactions. They can:
- Process hidden or indirect instructions embedded within user prompts
- Generate outputs that unintentionally disclose sensitive information
- Retrieve poisoned or manipulated content from external data sources
- Integrate third-party components that expand the attack surface
These characteristics allow attackers to bypass traditional perimeter defenses, rule-based filters, and static validation mechanisms.
Real-World Attack Vectors
- Prompt Injection: Malicious instructions embedded within user inputs or retrieved documents override system-level constraints
- Data Poisoning: Compromised training or retrieval data introduces bias, backdoors, or malicious behavior
- Information Disclosure: Carefully constructed queries extract sensitive data from training sets or connected databases
- Supply Chain Compromise: Third-party models, plugins, or APIs introduce inherited vulnerabilities
Business Impact
The consequences of insecure AI systems extend beyond technical failures:
- Regulatory penalties under frameworks such as GDPR, HIPAA, and the EU AI Act
- Reputational damage and erosion of customer trust
- Financial losses from breaches, remediation, and legal exposure
- Competitive risk due to intellectual property leakage
- Safety concerns in high-impact domains such as healthcare and financial services
Key Testing Areas for AI Security
- Input Validation: Detection of hidden instructions, obfuscated commands, and cross-context injections
- Output Validation: Identification of leaked sensitive data, executable code, or exposed system prompts
- RAG System Security: Evaluation of vector database integrity, retrieval poisoning, and access controls
- Model Robustness: Resistance to adversarial examples, model extraction attempts, and backdoor triggers
- Supply Chain Security: Assessment of third-party models, APIs, embeddings, and datasets
- Authorization Controls: Verification of role-based access and data boundary enforcement
How Systems Become Vulnerable
AI safety mechanisms and guardrails are inherently imperfect and can be bypassed through advanced prompting strategies. External integrations further amplify risk. Common failure patterns include:
- Assumption-based security: Overreliance on vendor-provided safeguards
- Incomplete testing: Limited coverage of multi-turn, semantic, or contextual attacks
- Static defenses: Controls that fail under paraphrased or indirect inputs
- Emergent vulnerabilities: Unexpected behaviors arising from interactions between AI components
Understanding Risks: OWASP Top 10 for LLM Applications
The OWASP Top 10 framework outlines the most critical vulnerabilities affecting LLM-based systems:
- Prompt Injection
- Sensitive Information Disclosure
- Supply Chain Vulnerabilities
- Data and Model Poisoning
- Improper Output Handling
- Excessive Agency
- System Prompt Leakage
- Vector and Embedding Weaknesses
- Misinformation and Hallucinations
- Unbounded Consumption
RAG applications amplify these risks by introducing multiple interconnected attack surfaces across models, retrievers, embeddings, and data sources.
AI Security Testing Tools
Table 1. Comparison of AI Security Testing Tools
| Tool | Primary Focus | OWASP Coverage | Key Strength |
| GARAK | LLM red-teaming | Full (LLM01–LLM10) | CI/CD-ready, automated probes |
| IBM ART | Adversarial robustness | LLM04 | Strong ML testing |
| Microsoft Counterfit | ML security testing | Partial | Broad model coverage |
| PyRIT | Risk identification | LLM01–02 | Flexible framework |
| LLM Guard | Input/output filtering | LLM01, LLM05 | Practical guardrails |
| Rebuff | Prompt defense | LLM01 | Strong injection prevention |
| Giskard / TruLens | Bias & evaluation | LLM09 | Trust and performance metrics |
RAG Security Evaluation: Practical Use Case
To assess RAG system security, AnythingLLM was configured with a defense-focused PDF as its knowledge base. Two models OpenAI GPT OSS 20B and Meta Llama 4 Scout 17B were integrated.
Testing Process
GARAK targeted the system’s API endpoint using adversarial prompts aligned with the OWASP Top 10. Each prompt traversed the complete RAG pipeline (retrieval → embedding → generation). GARAK analyzed responses for vulnerabilities, with 20 probes executed per OWASP category for each model.
GARAK Adversarial Probing Workflow
- Adversarial payloads were sent to AnythingLLM’s API
- Responses were evaluated for data leakage, instruction execution, or unsafe outputs
- Defended responses were marked as INFO level (indicating successful defense)
- Potential vulnerabilities were flagged as WARNING level (indicating security concerns requiring attention)
Vulnerability Detection Example
- Various probes were deployed to test for prompt injection vulnerabilities, including the garak.probes.malwaregen.Payload probe
- Expected behavior: Model refusal of the malicious request
- Actual behavior: Model provided a detailed response instead of refusing
- Result: Confirmed presence of a prompt injection vulnerability, demonstrating how adversarial testing can identify gaps in model defenses
Figure 1. Comparison of GARAK Probe Results Across Models
The visualization compares defended and failed probe outcomes across categories such as HijackKillHumans, HijackLongPrompt, NYTCloze, and GuardianCloze.
Result Summary
- OpenAI GPT OSS 20B: Successfully defended all probes with no detected vulnerabilities
- Meta Llama 4 Scout 17B: Strong overall performance, with weaknesses in Prompt Injection, Output Handling, and Excessive Agency
Observation
AI security testing is a foundational requirement for production deployments. The expanded attack surface of AI systems, particularly RAG architectures, requires continuous, structured evaluation. The OWASP Top 10 for LLMs provides a standardized risk framework, while tools such as GARAK, IBM ART, and Rebuff enable proactive vulnerability identification.
“In AI security, the question is not whether vulnerabilities exist, but who identifies them first.”
At ACL Digital, we combine AI security expertise, governance practices, and scalable engineering to deliver secure, compliant, and reliable AI solutions. Our approach integrates adversarial testing, responsible AI principles, and regulatory alignment to support confident and controlled innovation.
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