Exploited AI: How Attackers Hijack Generative AI and How AIShield GuArdIan Fights Back

Recent reports in cybersecurity have spotlighted a growing issue—large language models (LLMs) and other hosted AI models are increasingly being targeted for exploitation. For instance, in a report by Sysdig, attackers used stolen cloud credentials to exploit LLMs, resulting in unauthorized costs that exceeded $46,000 per day. Such breaches are not isolated incidents, with numerous cases of compromised AWS accounts being used to operate illicit AI chatbots, leading to significant financial and reputational damage for organizations (Krebs on Security). According to Sysdig, attackers have used stolen cloud credentials to exploit LLMs, leading to unauthorized costs exceeding $46,000 per day in some cases (Sysdig). These powerful AI tools, which hold great potential for businesses and creative applications, are also being misused by attackers using stolen cloud credentials.

For example, Krebs on Security highlighted how compromised AWS accounts have been used to operate illicit AI chatbots, leading to substantial financial and reputational losses (Krebs on Security). Additionally, MITRE ATLAS has outlined broader impacts of these attacks, including cost harvesting (unauthorized use of resources leading to significant financial loss), external societal harm (such as dissemination of inappropriate content via AI chatbots), and evading machine learning model safeguards (jailbreaking models to bypass ethical controls and misuse them). Whether it’s hijacking models to run illicit chatbots, exploiting their capabilities for unauthorized gain, or bypassing ethical safeguards, the challenges are mounting.

To further understand the sequence of actions taken by threat actors targeting AI models, we can refer to the kill chain concept used in cybersecurity. Below is an example kill chain for an incident, based on MITRE ATLAS Matrix:

1. Initial Access -> Valid Account (AML.TA0004 -> AML.T0012):

The attacker obtained an exposed long-lived AWS access key (AKIA), providing unauthorized access to cloud resources.

2. Discovery -> Discover ML Model Family (AML.TA0008 -> AML.T0014):

With initial access, the threat actor searched for available AWS Bedrock instances that could be exploited.

3. ML Model Access -> AI Model Inference API Access (AML.TA0000 -> AML.T0040):

The attacker gained access to the Bedrock instance in AWS, enabling further actions.

4. Execution -> Command and Scripting Interpreter (AML.TA0005 -> AML.T0050):

The attacker sent prompts including sexual roleplay and jailbreaking commands to manipulate the LLM into providing responses that were otherwise restricted.

5. Defense Evasion -> LLM Jailbreak (AML.TA0007 - AML.T0054):

Jailbreaking was embedded in the prompts to bypass internal guardrails of the LLM, allowing the generation of explicit content.

6. Impact -> Cost Harvesting (AML.TA0011 - AML.T0034):

Unauthorized access to the LLM resulted in significant costs, with potential expenses reaching up to $46,000 in a single day if not monitored.

• Additional Impacts -> Evade ML Model (AML.TA0011 - AML.T0015): Attackers successfully evaded the safeguards embedded in the ML model, enabling actions beyond ethical guidelines.
• Additional Impacts -> External Harms: Reputational Harm (AML.TA0011 - AML.T0048.01): The misuse of the LLM caused reputational damage to the affected organizations.
• Additional Impacts -> External Harms: Societal Harm (AML.TA0011 - AML.T0048.02): The incident had broader societal implications, such as the dissemination of harmful content.

This kill chain provides insight into the step-by-step tactics, techniques, and procedures (TTPs) used by attackers, emphasizing the need for robust security measures at every stage to prevent similar incidents. By understanding each phase of the attack, organizations can better anticipate potential threats and implement effective defenses to counter them before significant harm is done.

Let's dive into the key threats identified, understand the measures that experts recommend, and explore how AIShield GuArdIan emerges as a solution.

1. Credential Hijacking and Unauthorized Model Use: Attackers leverage stolen cloud credentials to gain unauthorized access to hosted LLMs. They exploit these credentials to utilize AI models for purposes ranging from explicit roleplaying bots to costly computation-intensive activities. As seen in cases highlighted by Krebs on Security and Sysdig, the damage extends beyond financial losses, potentially implicating the organizations that host these services in legal and ethical issues.

2. API Exploitation: Weaknesses in API security have made it easier for attackers to bypass safeguards and utilize AI models for unintended purposes. This kind of exploitation often involves using API endpoints to push harmful queries or bypass content moderation settings—turning these powerful tools into engines for inappropriate or malicious content.

3. Jailbreaking Models: Another rising concern is attackers “jailbreaking” AI models to circumvent their ethical and safety restrictions. This method has been used to manipulate AI-generated outputs beyond their intended ethical boundaries, enabling harmful use cases such as explicit content generation.

To counter these attacks, experts recommend several mitigation measures:

• Enhanced Credential and API Monitoring: Effective monitoring of API activity and cloud credentials is crucial to identifying misuse early. Logs should be analyzed for unauthorized access attempts.
• Role-Specific Policies: Implementing tailored access control based on user roles can help ensure that only authorized individuals can interact with AI models, limiting their misuse.
• Jailbreak Protection: AI systems should incorporate specific algorithms to detect and thwart any jailbreak attempts, preserving ethical guidelines and preventing models from generating harmful content.

AIShield GuArdIan is purpose-built to address these very challenges and ensure the secure deployment of generative AI in enterprise environments. Let’s take a closer look at how it aligns with the recommendations and leads the way in AI security.

AIShield GuArdIan uses dynamic policy mapping inspired by Identity-Access Management (IAM) systems. In practice, this means policies are customized based on specific applications, ensuring that only authorized applications can access certain AI functionalities. This role-based customization is like having different keys for different locks—only those with the right key can access specific features. This approach helps prevent unauthorized or unethical use, making the system more secure and aligned with organizational policies. By tailoring policies according to each application, GuArdIan ensures that only the right users have access to the right AI capabilities at the right time. Its Jailbreak Protection feature further strengthens security by actively detecting and stopping attempts to manipulate the behavior of LLMs beyond ethical boundaries, ensuring that AI outputs remain safe and compliant.

One critical recommendation was enhanced logging and real-time threat detection. AIShield GuArdIan provides real-time monitoring capabilities and comprehensive logging features. For example, it logs every API call made, tracking who accessed the model, when, and what data was used. This information helps in identifying suspicious activity early, ensuring that any unethical use is detected promptly. Such detailed logging not only aids in immediate threat response but also supports compliance audits by maintaining a clear record of all interactions with the AI system. These tools empower organizations to track AI activity, identify suspicious behavior, and act immediately. Additionally, AIShield GuArdIan connects with SIEM systems to activate Security Operations Center (SOC) alerts and initiate remediation processes. This integration helps ensure that any suspicious activity is promptly addressed, reducing potential damage. The logs also serve as a valuable asset for compliance audits, ensuring that the deployment of generative AI aligns with internal policies and regulations.

AIShield GuArdIan integrates seamlessly with cloud platforms such as AWS and Google Cloud. It operates within an enterprise’s Virtual Private Cloud (VPC), ensuring that sensitive data and AI resources are only accessible through highly secure environments. This feature mitigates the risk of credential hijacking by providing an additional layer of security that aligns access strictly with enterprise policies. By confining access to a dedicated VPC, GuArdIan makes it more difficult for attackers to exploit vulnerabilities, reducing the potential attack surface.

GuArdIan offers input and output management features that filter data to prevent harmful content generation, protect personal information, and enhance overall security. For example, input filtering ensures that prompts containing inappropriate language or malicious instructions are flagged or blocked before they reach the model. On the output side, responses are carefully reviewed to ensure they comply with company standards and ethical guidelines, such as avoiding the generation of sensitive information. This dual-layer of filtering helps maintain the integrity of AI responses, making sure that the system is both safe and compliant with regulatory requirements. These capabilities directly counter threats like inappropriate API use and help keep generative AI deployments within ethical and operational guidelines. For instance, input filtering ensures that potentially harmful prompts are flagged or blocked before they reach the model, while output filtering guarantees that responses comply with company standards and ethical policies.

Generative AI promises significant advancements for industries from healthcare to finance, but the risks associated with its deployment must be managed effectively. AIShield GuArdIan offers a comprehensive framework that not only protects these AI models but also facilitates their responsible use. With dynamic policy enforcement, real-time monitoring, and strong security features, GuArdIan helps enterprises unlock the potential of AI while ensuring ethical compliance and robust protection. The combination of advanced safeguards and seamless integration positions AIShield GuArdIan as a leading solution for organizations aiming to harness AI’s transformative power without compromising on security or ethics.

The journey of incorporating generative AI into enterprise systems is filled with potential but also fraught with risks. With AIShield GuArdIan, enterprises can navigate this landscape confidently—empowering innovation without compromising on security. Whether it’s preventing unauthorized use, blocking jailbreak attempts, or ensuring compliance, AIShield GuArdIan stands as a sentinel, safeguarding the integration of generative AI and allowing businesses to reap the benefits of AI-driven innovation, safely and responsibly. By providing comprehensive, enterprise-grade security, AIShield GuArdIan ensures that generative AI can be embraced to its fullest extent, unlocking new possibilities while keeping threats firmly at bay.

• Permiso Blog: "Exploiting Hosted Models" - Permiso.io
• Krebs on Security: "A Single Cloud Compromise Can Feed an Army of AI Sex Bots" - Krebs on Security
• Sysdig Blog: "LLMjacking: Stolen Cloud Credentials Used in New AI Attack" - Sysdig.com
• AIShield GuArdIan Overview and Capabilities - Bosch AIShield
• F5 Distributed Cloud Services Integration with AIShield GuArdIan - F5.com
• MITRE ATLAS: AI Threat Landscape Matrix - MITRE ATLAS