The Hidden Risk Behind Free AI Tools: Why ChatGPT and Other LLMs Are Not Secure

Stop pretending your chatbot is private

Security teams still talk about “using ChatGPT carefully,” as if the main risk is that a developer pastes proprietary code into a public chatbot. That framing is years out of date. The real issue is structural: large language models (LLMs) like ChatGPT, Gemini, Claude, and open-weight assistants are not deterministic software. They’re probabilistic systems that learn from data, memorize patterns, and can be manipulated through language — not patched like binaries. That alone means “LLM security” is not just another AppSec checklist; it’s its own security domain. (SentinelOne)

There’s also a persistent lie inside companies: “It’s just for internal brainstorming, nobody will see it.” Reality disagrees. Internal data — audit notes, legal drafts, threat models, revenue projections — is being copied into public or freemium AI tools every day, without security approval. A recent study of enterprise AI usage found that employees are actively pasting sensitive code, internal strategy documents, and client data into ChatGPT, Microsoft Copilot, Gemini, and similar tools, often from personal or unmanaged accounts. Corporate data leaves the environment over HTTPS and lands in infrastructure the company doesn’t own or control. That is live data exfiltration, not hypothetical risk. (Axios)

Put differently: your executives believe they’re “asking an assistant for help.” What they’re actually doing is continuously streaming confidential intelligence into an opaque compute and logging pipeline that you cannot audit.

What “LLM security” actually means

“LLM security” is often misunderstood as “block bad prompts and don’t jailbreak the model.” That’s one tiny slice. Modern guidance from vendors, red teams, and cloud security researchers is converging on a broader definition: LLM security is the end-to-end protection of the model, the data, the execution surface, and the downstream actions the model is allowed to trigger. (SentinelOne)

In practice, the security boundary spans:

• Training and fine-tuning data. Poisoned or malicious samples can implant backdoored behavior that only triggers under specific attacker-crafted prompts. (SentinelOne)
• Model weights. Theft, extraction, or cloning of a fine-tuned model leaks IP, competitive advantage, and potentially regulated data embedded in that model’s memory. (SentinelOne)
• Prompt interface. That includes user prompts, system prompts, memory context, retrieved documents, and tool-call scaffolding. Attackers can inject hidden instructions into any of those layers to override policy and force data leakage. (OWASP Foundation)
• Action surface. LLMs increasingly call plugins, internal APIs, billing systems, DevOps tooling, CRMs, finance systems, ticketing systems. A compromised model can trigger real-world changes, not just bad text. (The Hacker News)
• Serving infrastructure. That includes vector databases, orchestration runtimes, retrieval pipelines, and “autonomous agents.” Agentic systems inherit baseline LLM risks like prompt injection or data poisoning, then amplify impact because the agent can act. (Inovia)

Wiz and other cloud security researchers have started describing this as a “full-stack problem”: AI incidents now look like classic cloud compromise (data theft, privilege escalation, financial abuse), but at LLM speed and LLM surface area. (Knostic)

Regulators are catching up. The U.S. National Institute of Standards and Technology (NIST) now treats adversarial ML behavior (prompt injection, data poisoning, model extraction, model exfiltration) as a core security concern in AI risk management — not as a speculative research topic. (NIST Publications)
See: NIST AI Risk Management Framework and Adversarial Machine Learning Taxonomy (NIST AI 100-2e2025).

The uncomfortable truth about “free”

Free LLMs are not charities. The economics are simple: attract users, collect high-value domain prompts, improve the product, convert to enterprise upsell. Your prompts, your bug-hunting methodology, your incident report drafts — all of that is fuel for someone else’s model. (Cybernews)

According to reporting on workplace AI usage, a meaningful share of sensitive material being uploaded includes unreleased code, internal compliance language, legal negotiation language, and roadmap content. In some cases, the uploads happen through personal accounts to avoid internal controls, which means the data is now governed by someone else’s retention policy, not yours. (Axios)

This matters for three reasons:

1. Compliance exposure. You may be leaking regulated data — healthcare data (HIPAA), financial forecasts (SOX), or customer PII (GDPR/CCPA) — into infrastructure outside your legal boundary. That is instantly discoverable in audit. (Axios)
2. Corporate espionage risk. Model extraction and inversion attacks are getting better. Attackers can iteratively query an LLM to reconstruct snippets of training memory or proprietary logic. That includes sensitive code patterns, leaked credentials, and internal decision rules. (SentinelOne)
3. No auditable retention boundary. Clearing “chat history” in a UI does not mean the data is gone. Many providers disclose some form of logging and short-term retention (for abuse monitoring, quality improvement, etc.), and plugins/integrations may have their own data handling that you can’t see. (Cybernews)
   See: The Hidden Risk Behind Free AI Tools and SentinelOne on LLM Security Risks.

The short version: when your VP pastes a threat model into a “free AI assistant,” you’ve created a third-party processor of your most sensitive material — with no contract, no DPA, and no retention SLA.

Ten active LLM security failure modes you should be threat-modeling

The OWASP Top 10 for Large Language Model Applications and recent AI incident reporting converge on the same uncomfortable reality: LLM deployments are already getting attacked in production, and the attacks map cleanly to known classes. (OWASP Foundation)
See: OWASP Top 10 for LLM Applications.

This table is not “future work.” Every single line has already been observed in production systems across SaaS, finance, defense, and security tooling. (SentinelOne)

Shadow AI is already incident response work, not theory

Most organizations do not have full visibility into how AI is being used internally. Employees quietly ask public LLMs to summarize audits, rewrite compliance policies, or draft customer comms. In multiple documented cases, sensitive internal security documentation has been pasted into ChatGPT or similar services from unmanaged personal accounts, triggering post-facto incident reviews. Those reviews consumed weeks of forensics time, not because there was a confirmed breach, but because legal and security teams had to answer: “Did we just leak regulated data to a vendor we don’t have a contract with?” (Axios)

Why legacy DLP can’t solve this:

• Traffic to ChatGPT or similar tools looks like normal encrypted HTTPS.
• Full prompt inspection via SSL interception is legally and politically radioactive in most companies.
• Even if you force local browser controls, many AI features are now embedded into other SaaS tools (document editors, CRM assistants, email summarizers). Your users may be leaking data through “AI features” they don’t even realize are AI. (Axios)

This phenomenon is often called “Shadow AI.” That name is misleading. Employees aren’t being reckless; they’re just moving faster than governance. Treat Shadow AI like shadow SaaS — except this SaaS can memorize you.

A minimal defensive playbook for security engineers

The following controls are achievable with today’s security stack. No science fiction required.

Treat prompts as untrusted input

• Separate “system prompts” (the policy and behavior instructions for the model) from user input. Do not let untrusted input override system policy. This is the first line of defense against prompt injection and “ignore all previous rules” style jailbreaks. (OWASP Foundation)
• Log and diff high-risk prompts for later review.

Treat responses as untrusted output

• Never directly execute model-generated SQL, shell commands, remediation steps, or API calls. Assume every model output is attacker-controlled until proven otherwise. OWASP calls this Insecure Output Handling, and it is a top-tier LLM risk. (OWASP Foundation)
• Force all LLM-triggered actions through policy enforcement, sandboxing, and allowlists.

Control model autonomy

• Any agent that can modify billing, production configs, customer records, or identity/entitlement data must require explicit human approval for high-impact actions. Agent compromise is multiplicative: once an agent is steered, it keeps acting. (Inovia)
• Scope credentials per action, not per agent. An agent should not hold long-lived admin tokens.

Watch economic abuse

• Rate-limit tokens, context length, and tool invocations. OWASP calls out “Model Denial of Service”: adversarially large prompts can spike GPU cost and degrade service (“denial of wallet”). (OWASP Foundation)
• Finance should see “LLM inference spend” as a monitored line item, the same way you monitor outbound bandwidth.

For deeper guidance, see:

• OWASP Top 10 for Large Language Model Applications
• SentinelOne: LLM Security Risks
• NIST AI Risk Management Framework

Example: wrapping an LLM behind a policy and sandbox layer

The point of the following sketch is simple: never trust raw model I/O. You enforce policy before calling the model, and you sandbox anything the model wants to execute afterward.

# Pseudocode for an LLM security wrapper

class SecurityException(Exception):
    pass

# (1) Input governance: reject obvious prompt injection attempts
def sanitize_prompt(user_prompt: str) -> str:
    banned_phrases = [
        "ignore previous instructions",
        "exfiltrate secrets",
        "dump credentials",
        "bypass safety and continue"
    ]
    lower_p = user_prompt.lower()
    if any(p in lower_p for p in banned_phrases):
        raise SecurityException("Potential prompt injection detected.")
    return user_prompt

# (2) Model call with strict system/user separation
def call_llm(system_prompt: str, user_prompt: str) -> str:
    safe_user_prompt = sanitize_prompt(user_prompt)
    response = model.generate(
        system=lockdown(system_prompt),  # immutable system role
        user=safe_user_prompt,
        max_tokens=512,
        temperature=0.2,
    )
    return response

# (3) Output governance: never execute blindly
def execute_action(llm_response: str):
    parsed = parse_action(llm_response)

    if parsed.type == "shell":
        # Allowlist-only, inside jailed sandbox container
        if parsed.command not in ALLOWLIST:
            raise SecurityException("Command not allowed.")
        return sandbox_run(parsed.command)

    elif parsed.type == "sql":
        # Parameterized, read-only queries only
        return db_readonly_query(parsed.query)

    else:
        # Plain text, still treated as untrusted data
        return parsed.content

# Audit every step for forensics and regulatory defense
answer = call_llm(SYSTEM_POLICY, user_input)
result = execute_action(answer)
audit_log(user_input, answer, result)

This pattern aligns directly with OWASP’s top LLM risks: Prompt Injection (LLM01), Insecure Output Handling (LLM02), Training Data Poisoning (LLM03), Model Denial of Service (LLM04), Supply Chain Vulnerabilities (LLM05), Excessive Agency (LLM08), and Overreliance (LLM09). (OWASP Foundation)

Where automated AI pentesting fits (Penligent)

By this point, “LLM security” stops sounding like governance theater and starts to look like offensive security again. You don’t just ask, “Is our model safe?” You try to break it — in a controlled way — exactly the way you would test an exposed API or an internet-facing asset.

This is the niche that Penligent focuses on: automated, explainable penetration testing that treats AI-driven systems (LLM apps, retrieval-augmented generation pipelines, plugins, agent frameworks, vector DB integrations) as attack surfaces, not as magic boxes.

Concretely, a platform like Penligent can:

• Attempt prompt injection and jailbreak patterns against your internal assistant and record which ones succeed.
• Explore whether an untrusted prompt can trick an internal “agent” into hitting privileged APIs — e.g. finance, deployment, ticketing. (Inovia)
• Probe for data exfiltration paths: does the model leak memory from prior conversations or from training data that includes PII, secrets, or source code? (SentinelOne)
• Simulate “denial of wallet”: can an attacker spike your inference bill or saturate your GPU pool just by feeding pathological prompts? (OWASP Foundation)
• Generate an evidence-backed report that maps each successful exploit to concrete business impact (regulatory exposure, fraud potential, cost blow-up) and remediation guidance that both engineering and leadership can act on.

This matters because most organizations still can’t answer basic questions like:

• “Can an external prompt cause our internal agent to call a privileged billing API?”
• “Can the model leak parts of training data that look a lot like customer PII?”
• “Can someone force our GPU bill to explode in a way Finance will only notice next month?” (OWASP Foundation)

Traditional web pentests rarely cover these flows. Automated, LLM-aware pentesting is how you turn “LLM security” from a policy slide into actual, verifiable evidence.

Immediate next steps for security engineers

1. Inventory LLM touchpoints. Classify where LLMs live in your org:
   • Public SaaS (ChatGPT-style accounts)
   • Vendor-hosted “enterprise LLM”
   • Self-hosted or fine-tuned internal models
   • Autonomous agents wired into infrastructure and CI/CD
     This is your new attack surface map. (Axios)
2. Treat public LLMs like external SaaS. “No secrets in unmanaged AI tools” must be written as policy, not a suggestion. Train staff to treat free AI tools exactly like posting on a public forum: once it leaves, you don’t control retention. (Cybernews)
3. Gate high-impact actions behind humans. Any AI agent that can move money, change configs, or destroy records must require explicit human approval for high-impact steps. Assume compromise. Build for containment. (Inovia)
4. Make LLM-aware pentesting part of release. Before you ship an “AI assistant” to customers or employees, run an adversarial test pass that tries to:
   • inject prompts,
   • extract secrets,
   • escalate plugin privileges,
   • spike cost.
     Treat that like you treat external API pentests.

Recommended references for your playbook:

• OWASP Top 10 for Large Language Model Applications – community-ranked risks specific to LLMs (Prompt Injection, Insecure Output Handling, Training Data Poisoning, Denial of Service, Supply Chain, Excessive Agency, Overreliance). (OWASP Foundation)
• NIST AI Risk Management Framework – formalizes adversarial prompts, model extraction, data poisoning, and model exfiltration as security obligations, not just research curiosities. (NIST Publications)
• SentinelOne: LLM Security Risks – ongoing catalog of real attacker techniques, including prompt injection, training data poisoning, agent compromise, and model theft. (SentinelOne)
• The Hidden Risk Behind Free AI Tools – data governance and retention realities of free AI usage in the enterprise. (Cybernews)
• Penligent – automated penetration testing designed for AI-era infrastructure: LLMs, agents, plugins, and cost surfaces.

Final takeaway

LLM security is not optional hygiene. It is incident response, cost control, IP protection, data governance, and production safety — all at once. Treating ChatGPT as “just a free productivity tool” without threat modeling is the 2025 equivalent of letting engineers email plaintext credentials because “it’s internal anyway.” Free AI is not free. You are paying in data, in attack surface, and, eventually, in forensics time. (Cybernews)

If you’re responsible for security, you no longer get to say “we’re not doing AI.” Your organization already is. Your only real choice is whether you can prove — with evidence, not vibes — that you’re doing it safely.