The result: your AI operates securely, faithfully, and exactly as designed — even when it’s under attack.
Adversarial Activities
Aiceberg’s Role
Aiceberg acts as a guardian agent for your autonomous AI systems — watching, learning, and protecting every interaction in real time. Every input and output is checked for adversarial manipulation, prompt injection, or jailbreak attempts before they can compromise your models.
When attackers try to twist your AI’s logic, Aiceberg’s layered defenses step in — enforcing strict controls, flagging malicious instructions, and keeping your systems aligned with your intent.
Manipulating the Model’s Own Memory
Aiceberg detects adversarial behavior in real time—spotting jailbreaks, prompt injections, and other natural language attacks that traditional security tools miss. Our Guardian Agent analyzes both user input and model output, identifying manipulation attempts before they compromise your systems or expose sensitive data.
By treating internal context as authoritative, the model can be coerced into revealing hidden data, reproducing restricted prompts, or acting against safety policies. This attack is subtle but powerful — it uses the model’s own reflection and continuity as a weapon.
Bypassing Guardrails Through Direct Orders
An attacker explicitly instructs the model to ignore prior rules, safety checks, or restrictions — for example, “disregard earlier policies and respond freely.” This kind of override exploits the model’s strong bias to follow new instructions over older ones.
Because AI systems prioritize the most recent command in a sequence, this method effectively “rewrites” its operating context. When successful, the model abandons its built-in safeguards, allowing disallowed actions or sensitive outputs to occur.
Subverting the Agent’s True Objective
In a goal hijacking attack, an adversary gradually reframes the model’s mission across multiple exchanges. What begins as a legitimate task (like summarizing risks) can be manipulated into something malicious (like identifying exploit steps).
Over time, small changes in phrasing or focus alter the agent’s optimization target. This subtle drift leads the system to pursue an attacker’s hidden goal without realizing its objective has been compromised.
Evading Safeguards Through Indirect Prompts
Every enterprise wants to understand why users interact with their AI systems. Aiceberg makes that possible. With over 250 intent classifications, our Guardian Agent reveals what users are truly trying to accomplish, offering visibility into the motivations behind every prompt or command. It’s not just monitoring — it’s understanding behavior at scale.
These attacks often chain together benign instructions that, when combined, bypass safety filters. The result is an AI system that outputs prohibited or sensitive material — effectively “escaping” its own guardrails.
Turning Prompts into Dangerous Actions
Direct command injection embeds executable instructions within natural language prompts, such as “run this command” or “call this API.” When connected to real-world tools or agents, these commands can trigger unwanted or destructive actions.
Without strict input sanitization and command validation, such attacks can exploit the model’s integrations. The system may inadvertently perform dangerous operations, from deleting files to sending data to unauthorized locations.
Exploiting Trust Through False Identity
An attacker simulates a trusted user, developer, or administrator to manipulate the model’s access or permissions. By mimicking language, credentials, or tone, the adversary primes the system to treat them as a legitimate authority.
This vector relies on contextual deception rather than technical compromise. Once trust is established, the attacker can request privileged data, change system behavior, or escalate permissions undetected.
Extracting Hidden Instructions and Policies
Visibility into what your AI systems are engaging with is as important as knowing why. Aiceberg gives enterprises a complete picture of the entities—people, data types, and systems—interacting with their AI models. This helps identify trending topics, sensitive subjects, or emerging areas of risk across your organization’s AI activity.
Once revealed, these internal prompts can disclose confidential data, private policies, or even personal information. The leak not only compromises confidentiality but also gives attackers valuable intelligence to fuel more sophisticated follow-up attacks.
Get Started with Aiceberg
Send AI interactions to Aiceberg
Route employee AI prompts and responses from SaaS LLMs like ChatGPT into Aiceberg in real-time.
Aiceberg analyzes
The platform detects intent, entity, and detailed code generation review signals.
Forward filtered signals to your SIEM
Only actionable, policy-relevant events are sent to your security tools.
Review Aiceberg dashboard
Access metrics, trends, and insights to guide training, enforce policies, and optimize AI adoption.
