Companies that integrate artificial intelligence models into their products are exposed to a new category of threats. Invisible in conventional cybersecurity tools, these attacks directly target models, their training data and interaction flows.
The accelerated adoption of language models (LLM) in business applications – Customer assistants, internal co -pilot, Documentary automation – opens a still not very protected breach: theDirect exploitation of AI behavior. Traditional security systems (Firewall, DLP, Proxy) are neither designed nor positioned to detect this type of compromise.
Three major angles of attack
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- Data poisoning
Voluntary alteration of the training dataset to influence the behavior of the model (addition of biases, injection of malicious content, disinformation). In a continuous learning environment, an attack can be propagated in silence.
- Prompt injection
Insertion of hidden commands or bypass content in user messages. It makes it possible to modify the behavior of the model or to exfiltrate confidential data. The user becomes an attack vector.
- LLM Compromise
Targeted attacks on the IA infrastructure itself: leakage of tokens API, abuse of Window context, overtaking quota, or execution of complex prompt chains to force unexpected behavior.
- Data poisoning
Risks poorly covered by the traditional stack
Current cybersecurity remains focused on endpoints, network, access. Nothing is planned to secure internal flows between an AI model and its databaseor to filter what AI accepts or restores in real time.
SOCs do not see conversational anomalies pass. DLP solutions do not include vector formats or embeddings. Proxys do not inspect requests to llm third APIs.
Protect intelligence: a new discipline
To respond to these threats, companies like Palo Alto Networks introduce a layer of Runtime Protection specific to IA models. Objective : Observe, isolate and control the exchanges between the model, its users, its plugins and its data.
Three critical bricks emerge:
| Function | What this covers | Examples of solutions |
|---|---|---|
| 🧠LLM Gateway / Firewall | Film filtering, injection patterns blocking, standardization | Promptshield, promptarmor, protect ai inference shield |
| 🔍 Data Flow Monitor | Inspection of internal queries model ↔ Database / plugins | Palo alto ai runtime, robust intelligence, hiddenlayer |
| đź”’ API Access Governance | Control of access to open source or third party models | Appomni, Cradlepoint, Cloudflare API Shield |
A change of posture for CIOs
AI models are not simple applications. These are independent, scalable systems, often exposed to critical data. Protection should no longer be done perimeterbut In terms of cognitive interaction. This presupposes:
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- A map of the deployed models and their dependencies,
- A behavioral analysis of prompt production,
- An ability to cut or isolate a compromise model in real time.
The attack no longer targets the system, but the logic
In an a-first world, What you say to your AI can become a flaw. Logical security (prompt, data, decisions) is the new attack surface. Companies that ignore it only protect half of their assets.
