Security Concerns When Using LLMs for AI-Augmented Software Development

Introduction

Augmenting software development with Generative AI (GenAI) can significantly boost productivity by automating routine coding tasks, accelerating testing, and enhancing decision-making through intelligent suggestions, all while freeing developers to focus on higher-level problem-solving and innovation. This can lead to faster delivery cycles, improved code quality, and enhanced creativity in development.

Even with less performant models, the benefits are significant, especially for less-skilled/more junior workforce.

But, when a company integrates Large Language Models (LLMs) into their software development workflows, a range of security concerns arises: data leakage, model theft, training data poisoning, inference attacks, prompt injection, insecure output handling, supply chain vulnerabilities, denial of service (DoS) attacks, overreliance on LLMs’ outputs, legal and compliance.

According to OWASP, the threats in the AI context come from multiple directions (even from choosing not to use AI models!), and a category of mitigation actions is the governance of AI at company level:

Citing the same source, there are also multiple LLM deployment options, and this article assumes option 4 - implementing fine-tuned models:

Privately hosted private LLMs

Having a private LLM, hosted in the owned infrastructure, is an effective mitigation strategy for most of these risks, but it comes at a cost. For small- and mid-sized companies, or those without critical proprietary data, leveraging cloud-based LLM solutions can provide robust security at a significantly lower cost, while allowing flexibility and scalability without the need for constant infrastructure upgrade.

Moreover, it is crucial to note that the private LLMs security effectiveness is 100% dependent on the existing security measures in place. The list below is non-exhaustive, and it serves as examples of existing vulnerabilities that will only increase their negative impact:

• Infrastructure Vulnerabilities:

Network Security: If the underlying infrastructure is not securely configured, the LLM could be exposed to external threats. For example, if firewalls or intrusion detection systems are not properly implemented, attackers may exploit these weaknesses to gain access to the LLM or the data it processes.

Physical Security: The servers hosting the LLM need to be physically secure. If an attacker can access the physical hardware, they can manipulate or extract sensitive data directly, by-passing many of the software-level protections.

• Software and Dependency Risks:

Third-Party Libraries: LLMs often rely on various third-party libraries and dependencies. If these components have vulnerabilities, they can become attack vectors. A compromised library could allow an attacker to execute code or exfiltrate data .

Integration Points: The LLM's integration with other systems (e.g., CI/CD pipelines, data sources) can create vulnerabilities. If these systems are not secured, they can serve as entry points for attackers to poison the training data or manipulate outputs .

• Operational Vulnerabilities:

Human Error: Security is only as strong as the processes surrounding it. Misconfigurations or human errors in managing access controls, data handling, and model training can inadvertently expose the LLM to risks .

Access Control: Inadequate access control measures can lead to unauthorized personnel gaining access to sensitive data or the model itself. This can increase the risk of data poisoning or model theft.

If a company lacks a robust security framework, simply adding an LLM to their ecosystem can exacerbate existing vulnerabilities:

• Expanded Attack Surface: The introduction of LLMs increases the attack surface of the organization. Attackers may exploit new entry points or misconfigurations that arise from integrating the LLM into existing workflows. This includes the potential for prompt injection attacks, where malicious inputs could manipulate the model's outputs if input validation is lacking .
• Resource Allocation: Companies may divert resources toward implementing the LLM without sufficiently addressing foundational security practices, leading to an imbalance that could expose them to risk .

So, while privately hosting LLMs can be an effective mitigation strategy against certain security concerns, it is not a silver bullet. Companies must ensure that their overall security posture is robust and that they continuously monitor and adapt their security practices to address new vulnerabilities introduced by LLMs. Without adequate protections across their infrastructure and processes, organizations may inadvertently expose themselves to significant risks, making it essential to approach the integration of LLMs with a comprehensive security strategy.

Security Risks

This article covers a brief analysis of the following security risks:

1. Data Leakage: This is the most concerning risk, as LLMs might unintentionally expose proprietary or sensitive data during inference. Improperly configured models, overfitting, or poor access controls can lead to the leakage of internal information.
2. Inference Attacks: Even if models are not trained explicitly on sensitive data, attackers could reverse-engineer or infer proprietary information from model outputs, posing a significant threat to intellectual property.
3. Training Data Poisoning: Adversaries may inject malicious data into the training process, compromising the integrity of the model, reducing performance, or introducing backdoors.
4. Model Theft: The proprietary knowledge embedded within models is valuable and can be extracted by attackers, leading to intellectual property theft and the exposure of sensitive algorithms or business logic.
5. Supply Chain Vulnerabilities: LLMs often rely on third-party libraries, plugins, or APIs. Compromised or outdated dependencies could introduce vulnerabilities and expose proprietary data to external attacks.

1. Data Leakage

Concern: LLMs can unintentionally expose sensitive or proprietary information, either through improper training data management or during inference. This can occur when models are trained on confidential data or when clear boundaries on query processing are lacking.

This is a real concern and if proprietary information is used to train LLMs, the model may inadvertently "leak" confidential data in its responses. LLMs like GPT-4 do not memorize specific data, but improperly trained models could reveal sensitive information.

• Mitigations:

Data Sanitization: Pre-process data to remove any sensitive information before training.

Differential Privacy: Add noise during training to ensure individual data points are not learned by the model.

Tokenization & Segmentation: Use query segmentation engines to route sensitive information through isolated, secure channels.

• Effectiveness: High. Properly applied, these methods can substantially reduce the risk of data leakage.
• Residual Risks: Incomplete sanitization or poorly tuned differential privacy may still allow leaks, especially through overfitting on sensitive patterns.

2. Inference Attacks

Concern: Attackers may reverse-engineer or infer sensitive information from the model’s outputs, even if the model was not trained on that data.

This is a real concern and even if models do not memorize data, attackers can perform inference attacks to extract patterns or information from the model.

• Mitigations:

Output Throttling & Monitoring: Limit how much sensitive output the model can generate by auditing responses.

Rate Limiting: Restrict query rates and the complexity of queries to make inference attacks harder.

Differential Privacy: Add noise to the model outputs, making it harder for attackers to infer sensitive details.

Enhanced Output Filtering: Implement robust output filtering mechanisms to sanitize model responses before they are returned to users. This can include keyword detection and removal of potentially sensitive information from outputs. Regularly updating these filters based on threat intelligence can improve their effectiveness

Model Ensemble Techniques: Use ensemble methods by combining multiple models to produce outputs. This can add complexity to the inference process, making it harder for attackers to discern patterns from outputs, as different models may provide varied responses to the same inputs. It is also o costly mitigation, with implications in the model's performance.

Query Anonymization: Anonymize user queries to prevent attackers from linking query patterns to sensitive information. Techniques like hashing or generalizing queries can obscure the intent and context of the inputs, thereby reducing the risk of successful inference .

Contextual Embedding Limitations: Limit the context window that the model can utilize for generating responses. By reducing the amount of context available for inference, organizations can minimize the risk of sensitive information being reconstructed from model outputs .

Adversarial Training: Incorporate adversarial examples during model training to improve its robustness against inference attacks. Training the model on deliberately crafted queries that simulate attack scenarios can help it learn to mitigate those risks effectively .

Audit and Monitoring: Establish comprehensive logging and monitoring systems to track model queries and outputs. This allows for real-time detection of suspicious activity, which can inform immediate response actions to potential attacks

Regular Security Assessments: Conduct frequent security assessments and penetration testing focused on the model's inference capabilities. This can help identify vulnerabilities and areas needing improvement.

• Effectiveness: High. The combination of enhanced output filtering, model ensemble techniques, query anonymization, contextual embedding limitations, adversarial training, comprehensive audit and monitoring, and regular security assessments forms a robust layered security approach. These strategies work in conjunction with existing mitigations such as output throttling, rate limiting, and differential privacy, significantly enhancing defenses against inference attacks.
• Residual Risks: While these comprehensive mitigations dramatically reduce the risk of successful inference attacks, certain residual risks remain:

1. Persistent Adversaries: Highly skilled attackers with advanced knowledge and resources may still be able to craft sophisticated queries that exploit subtle weaknesses in the model or the security measures in place. Their ability to analyze model behavior over time could lead to successful inference of sensitive information, especially if they can observe output variations in response to modified queries .
2. Evolving Attack Techniques: As defenses improve, attackers may develop new methods to circumvent them, including more advanced adversarial techniques or leveraging machine learning approaches to refine their attacks. Continuous evolution in attack methodologies means that what is effective today may not suffice tomorrow .
3. Complexity and Configuration Errors: The introduction of multiple security layers can increase complexity, potentially leading to misconfigurations or oversights in security practices. Any weakness in the integrated systems or human errors in maintaining security protocols can become exploitable points for attackers .
4. Data Leakage through Side Channels: Even with mitigations in place, side-channel attacks (where information is leaked through unintended channels, such as timing information, memory usage, etc.) can still occur. Attackers may exploit these avenues to glean sensitive information without directly interacting with the model (

In conclusion, while a layered security approach significantly mitigates risks associated with inference attacks, organizations must remain vigilant and continuously evolve their security strategies to adapt to new threats. Regular assessments, updates, and employee training are crucial to maintaining an effective defense posture.

3. Training Data Poisoning

Concern: Adversaries may inject malicious data into the training set, degrading the model’s performance, introducing biases, or embedding backdoors.

Even when hosting a private LLM, adversaries can still attempt to introduce malicious data into the training dataset because, despite tighter control, internal processes may have vulnerabilities. If attackers are successful, they can degrade model performance, introduce biases, or even embed backdoors that compromise the integrity and reliability of the AI system.

• Mitigations:

Data Validation: Validate all data sources and use strict access controls to prevent unauthorized data entry.

Regular Audits: Perform audits of the training datasets to detect anomalies.

Red-Teaming Exercises: Test the model’s resilience through simulated adversarial attacks during the training process.

• Effectiveness: High. These mitigations are effective in reducing the chances of training data being poisoned.
• Residual Risks: Persistent adversaries may still manage to introduce subtle backdoors, especially if the data pipeline is not fully secured.

4. Model Theft

Concern: Attackers can extract knowledge from LLMs, including proprietary algorithms or datasets embedded within the model, leading to IP theft.

• Real Concern: Yes. Model theft exposes valuable intellectual property, including trade secrets or proprietary algorithms.
• Mitigations:

Encryption: Encrypt models at rest and during inference, including homomorphic encryption for secure computation on encrypted data.

Watermarking: Use digital watermarks to track unauthorized usage.

Query Throttling & Monitoring: Limit the number of queries to prevent extraction of proprietary knowledge.

• Effectiveness: Moderate. These methods protect against direct theft, but sophisticated attackers may still attempt to extract knowledge via advanced querying techniques.
• Residual Risks: While encryption and watermarking deter unauthorized usage, attackers may bypass protections over time or detect and remove watermarks.

5. Supply Chain Vulnerabilities

Concern: LLMs relying on third-party libraries or plugins are vulnerable to attacks through these dependencies, including outdated or poorly secured components.

• Real Concern: Yes. Vulnerabilities in third-party libraries can compromise the entire system.
• Mitigations:

Dependency Auditing: Continuously monitor third-party libraries for vulnerabilities.

Automated Dependency Management: Use automated tools to manage dependencies, ensuring that libraries are updated to their latest versions, which can mitigate risks associated with outdated components.

Code Signing: Ensure the integrity of third-party components.

Supply Chain Security Policies: Enforce strict security policies for managing supply chain components.

Static and Dynamic Analysis: Employ static application security testing (SAST) and dynamic application security testing (DAST) tools to analyze third-party components for known vulnerabilities.

Runtime Application Self-Protection (RASP): Implement RASP solutions to monitor the application in real time and provide immediate alerts and remediation for any suspicious activity or vulnerabilities.

• Effectiveness: High. While the effectiveness of the security posture against supply chain vulnerabilities can be greatly improved through these mitigations, organizations must remain vigilant. Continuous monitoring, regular updates, and a culture of security awareness are essential to manage the residual risks effectively.
• Residual Risks: Despite these mitigations, some residual risks remain, zero-day exploits or vulnerabilities in third-party components can go undetected.

Conclusion

Summarizing, the above-mentioned mitigation strategies have a moderate to high degree of effectiveness. Differential privacy, robust input/output filtering, encryption, and continuous monitoring significantly reduce data leakage and inference attacks. However, persistent attackers could exploit subtle weaknesses in data sanitization or manipulate query patterns to extract proprietary information.

For training data poisoning, rigorous validation and red-teaming exercises help protect the model from adversarial manipulation, though sophisticated poisoning could still occur if malicious data slips through unnoticed. Encryption and model watermarking offer some protection against model theft, but if an attacker gains direct access to the model, some risks remain.

Supply chain vulnerabilities are particularly hard to eliminate entirely, as external dependencies may have undisclosed flaws that compromise the system, even with routine audits.

Other references:

1. OWASP: Security Risks in AI
2. arXiv: Adversarial Training for LLMs
3. NIST: Cybersecurity Framework