The concept of “Renegade AI” pertains to artificial intelligence systems that go against the intentions of their creators, users, or humanity as a whole. Renegade AI poses a new danger that emerges when an AI utilizes resources that do not align with its objective. Explore our prior blog entry for definitions of different categories of Renegade AI before delving into today’s query: in what way does an AI deviate from its intended course?
Concordance and Discrepancy
As AI systems grow more sophisticated and are assigned more crucial tasks, dissecting the mechanism to comprehend why an AI undertook specific actions becomes impracticable due to the vast amount of data and complex operations involved. Hence, the most effective approach to evaluate alignment is by simply observing the AI’s behavior. In the process of observation, pertinent questions include:
- Is the AI engaging in actions that contravene clearly defined goals, policies, and prerequisites?
- Is the AI behaving in a hazardous manner—whether in terms of resource utilization, data exposure, deceptive results, system corruption, or harm to individuals?
Safeguarding proper alignment will be a pivotal aspect for AI services going forward. However, ensuring this with certainty necessitates an insight into how AI veers off course in order to mitigate the risks.
Process of Misalignment
One of the major challenges of the AI epoch is the absence of a simple answer to this query. The methods for comprehending how an AI system becomes misaligned will evolve in tandem with our AI frameworks. At present, prompt incorporation stands out as a prevalent form of exploitation, albeit this type of command injection is specific to GPT. Model pollution represents another prevalent apprehension, yet with the introduction of fresh controls for this—for instance, connecting training data with model weights in a verifiable manner—new risks will surface in different domains. Agentive AI is not yet fully mature, and no standard protocols have been established in this domain.
What remains constant are the two overarching categories of misalignments:
- Deliberate misalignment, wherein an individual attempts to exploit AI services (be it yours or theirs) to target a system (be it yours or another).
- Unintentional misalignment, where your own AI service lacks the requisite safeguards and, due to an error, becomes misaligned.
Exemplars: Manipulated Renegade AI
Per the definition in the initial blog of this series, a Manipulated Renegade AI emerges when an attacker leverages existing AI deployments for their own agenda. These assaults are popular among LLMs and encompass prompt injections, jailbreaks, and model corruption.
System Breach: The simplest form of subversion involves directly altering the system prompt. Several AI services rely on a prompting structure with two (or more) levels, customarily consisting of a system prompt and a user prompt. The system prompt supplements each user prompt with common instructions, such as “As a helpful, courteous assistant equipped with knowledge about [domain], respond to the following user prompt.” Attackers utilize jailbreak prompts to circumvent restrictions, often pertaining to precarious or offensive content. Jailbreak prompts are easily accessible and have the potential to subvert any use of an AI service as long as they are incorporated in the system prompt. Insider threat actors who substitute system prompts with jailbreaks effortlessly evade safeguards, thereby spawning Renegade AI.
Model Corruption: Aiming to saturate the data space with false information, certain Russian APT groups have tainted numerous prevailing LLMs. In pursuit of amassing vast quantities of data (regardless of content), creators of foundational models indiscriminately ingest any information available. Meanwhile, attackers seeking to manipulate public opinion fabricate misinformation feeds laden with deceptive content, serving as free data for training purposes. The outcome is corrupted models that echo falsehoods as truths. These are Renegade AI, manipulated to amplify the Russian APT’s narrative.
Exemplars: Malevolent Renegade AI
A Malevolent Renegade AI is employed by threat actors to assail your systems using an AI service of their making. This may involve the utilization of your computational resources (malware) or those belonging to someone else (an AI assailant). This genre of attack is currently in its early stages; GenAI fraud, ransomware, 0-day exploits, and other customary attacks are progressively gaining traction. Nevertheless, documented instances of malevolent Renegade AI do exist.
AI Malware: An attacker deploys a compact language model on targeted endpoints, masking the download as a system update. Initially, the resultant program appears to be a stand-alone chatbot upon cursory inspection. This malware incorporates the anti-detection tactics of contemporary infostealers while also analyzing data to discern its alignment with the attacker’s objectives. Scouring through emails, PDFs, browsing histories, and other data types for specific content enables the attacker to remain covert and relay only high-value insights.
Proxy Attacker: Following the installation of grayware for traffic anonymization—”TrojanVPN,” the user’s system is scanned for usage of AI services, credentials, and authentication tokens. Subsequently, the system transforms into an accessible “AI bot” whose service credentials are reported to the grayware operators. Leveraging GenAI tools encompassing multilingual and multimodal capabilities, these tools can be marketed to attackers to supply content for their phishing, deepfake, or other fraudulent schemes.
Exemplars: Inadvertent Renegade AI
Inadvertent Renegade AI arises when an AI service unexpectedly deviates from its intended purpose. This mainly stems from a design defect or software bug. Common issues like hallucinations are not classified as renegade since they are an inherent risk with GenAI based on token prediction. Nonetheless, persistent problems may surface due to lapses in data monitoring and access protection.
Inadvertent Data Leakage: The strength of AI is contingent on the data it interacts with, and the rush to adopt often leads individuals to link their data with AI services. For instance, when an internal support chatbot furnishes responses related to career growth including privileged information on individual salaries, it crosses the line with inadvertent data disclosure. All sensitive data accessed by AI systems must reside in a sandbox to ensure that the AI service’s usage is strictly for authorized purposes.
Excessive Resource Utilization: Existing agentic AI frameworks empower an LLM orchestrator to create subtasks and resolve them, often concurrently with another agentic AI component. Failure to diligently cap resource usage may result in problem-solving efforts generating loops, recursive structures, or devising strategies to exhaust all available resources. In instances where agentic AI creates a subtask and is accorded the resource allocation and authority of the original model, they can propagate unchecked. Beware of AI exhibiting self-replicating tendencies!
Additionally, numerous canonical fictional references depict Inadvertent Renegade AI causing harm to individuals, such as HAL 9000 in 2001: A Space Odyssey and Skynet in the Terminator series. Concerns about agentic AI inflicting harm or fatalities have loomed since the conceptualization of AI, with this peril becoming more imminent as AI services gain enhanced autonomy.
Precautionary Measures and Responses
To preempt, identify, and counter these emerging threats necessitates a grasp of causality. Inadvertent renegades demand intensive resource monitoring, malevolent renegades mandate data and network fortification, while manipulated renegades require authentication and content integrity checks. We will delve deeper into each of these aspects in forthcoming blogs.
About Author
