Yes, another blog article about AI and cybersecurity… but it’s a topic that remains crucial. In this article, we’ll explore how generative AI (Gen AI) tools create new attack surfaces and are susceptible to various types of cyber threats. Whether you’re a vendor of a Gen AI solution or you integrate these tools into your applications, your Gen AI prompt fields or chat boxes have become the latest playground for hackers, presenting a fresh security challenge for your SecOps teams. The effectiveness and performance of Gen AI tools rely on users’ ability to input free text and the AI’s capability to process it. This inherently limits the restrictions you can impose on Gen AI input fields, making them a significant security concern.
Here are some of the attacks we often see on Gen AI apps and Gen AI tools embedded in enterprise applications.
1. Resource Exhaustion (Denial of Service)
Gen AI systems are resource-intensive by design, making them prime targets for resource exhaustion attacks.
- High-Volume Queries: Attackers flood Gen AI systems with massive numbers of rapid-fire requests causing service disruptions or degraded performance.
- Infrastructure Overheads: Excessive resource consumption, including CPU, memory, and bandwidth, can lead to increased operational costs and high Total Cost of Ownership.
2. Prompt Injection and Exploitation
The open-text nature of Gen AI prompt fields provides attackers with a direct avenue for malicious inputs.
- Exploiting Vulnerabilities: Malformed or malicious prompts are designed to probe for weaknesses, manipulate AI behavior, or bypass established safeguards.
- Command Injection: Injecting executable code or malicious instructions into prompts can compromise AI responses or improperly interact with backend systems, leading to potential data breaches.
3. Undermining AI Response Integrity
Adversaries or even regular users with malicious intent can disrupt or manipulate the reliability of AI-generated outputs, potentially eroding trust in the system. This can occur through deliberate probing or by exposing underlying weaknesses in the AI model.
- Revealing Weaknesses: Repeated querying by attackers—or overly curious users can expose model patterns, biases, or operational limitations. For instance, users may discover that the model consistently fails to handle certain types of inputs or exposes unintended logic. This could erode trust in the system's reliability and fairness.
- Triggering Failures: Feeding edge-case or adversarial inputs can crash models, corrupt data, or inadvertently disclose sensitive internal information to attackers or overly curious users who exploit the system’s vulnerabilities.
4. Privacy Violations via Account Takeover (ATO)
The integration of Gen AI tools with user and employee data creates new opportunities for attackers to exploit these tools as an entry point for ATO attacks. Hackers can abuse Gen AI prompts to access sensitive information, which can then be leveraged to compromise accounts.
- ATO of Customer Accounts: Unauthorized access to customer accounts through ATO allows attackers to exfiltrate sensitive information, leading to privacy violations and potential legal repercussions.
- ATO of Employee Accounts: Attackers targeting employee accounts may gain access to proprietary or confidential enterprise data, causing reputational harm and financial loss.
5. API Abuse (Exploitation of API Vulnerabilities)
The reliance on APIs to power Gen AI tools introduces another layer of vulnerability.
- Targeting API Weaknesses: Gen AI APIs exposed to user interactions are often susceptible to exploitation, such as excessive calls, injection attacks, or manipulation of API logic to extract sensitive information or disrupt operations.
- Data Poisoning: GenAI tools embedded in applications connect to various internal and external databases via APIs to draw data from. Attackers can hack those databases to feed the LLM with malware, fake data, malicious scripts, worms and nefarious URLs, which will be distributed via the Gen AI tools to legitimate users. These attacks damage the application’s reputation as well as put it in breach of regulatory standards and cause substantial financial damages due to litigations, fines, and penalties.
Challenges Traditional Application Protection tools (e.g. WAF, WAAP) Face in Protecting Integrated Gen AI Tools and Their Prompt Input Fields:
Free Text Prompts: The allure of Gen AI tools is their ability to process and respond to free text. However, this flexibility poses a challenge: how to block illegitimate prompts without hindering legitimate user input. Implementing security rules that strike this balance is crucial.
Database Accessibility: Gen AI tools often connect to sensitive databases, making robust cyber hygiene essential to minimize the risk of exposing sensitive data.
Single Attackers: Unlike distributed bot attacks, single human attackers using manual injections are harder to detect. Without established baselines, distinguishing between malicious and legitimate prompts becomes more challenging.
What to Look for in an Application Protection Solution to Safeguard Your Gen AI Tools:
You need AI to fight AI; otherwise, by doing things manually, you’ll always be on the losing side of that cat-and-mouse game, and you won’t be able to maintain a fast MTTR (Mean Time To Resolution). It’s recommended to implement an AI-driven, multi-layered approach that includes:
- Real-time intelligence feeds of known attackers, IPs, and identities integrated with a WAF to automatically block unwanted requests and API calls.
- AI-driven detection of sophisticated bots that rotate IPs and identities while communicating with your embedded Gen AI tools.
- AI-powered analysis of API business logic to detect and block anomalous behavior and prompts in real-time.
- An AI-driven SecOps solution that provides on-the-fly root cause analysis to minimize Mean Time to Resolution (MTTR).
- Cross-correlation of different protection layers to quickly identify and mitigate malicious actors trying to abuse the Gen AI prompt fields or chat boxes.