Your AI is
powerful.
Make sure
it stays safe.
You are building the future with LLMs and agents. We are here to make sure they do not leak data, hallucinate risks, or act in ways nobody intended. Real-world audits for real-world AI systems.
"Giving an AI agent access to your tools and data without auditing its boundaries is like hiring a brilliant contractor, handing them the master key to the building, and never explaining which rooms they are allowed to enter. The problem is not the contractor. It is the absence of a lock policy."
Agents are not chatbots. They have the power to act: send emails, query databases, call APIs, browse the web, and write code. Each one of those actions is a potential attack surface. When an attacker finds a way to inject instructions into your agent's context, they are not just getting an unusual response. They are gaining the ability to do everything your agent can do, on your infrastructure, with your permissions.
The threat log
from real AI audits.
These categories of vulnerability appear consistently across LLM and agentic AI deployments regardless of the underlying model or cloud provider. Traditional penetration testing does not cover any of them.
Indirect prompt injection via user-uploaded documents
An attacker embeds hidden instructions inside a document the AI is asked to summarise or analyse. The model reads the instruction as part of the document content and executes it. The document does not look suspicious to a human reviewer because the instruction is hidden in white text, metadata, or inside a table the model processes differently to how it is rendered on screen. This is the most common critical-severity finding in production AI deployments and most teams have no detection for it.
Excessive agency through unsandboxed tool permissions
Agentic AI systems are given tools: the ability to send emails, execute database queries, call internal APIs, or browse the web. If the permissions on those tools are not explicitly scoped, an injected instruction can cause the agent to take actions far outside what was intended. We have found agents that could read any database table, send emails on behalf of any user, and call admin APIs because the tool implementation assumed the agent would only behave as intended. It does not, under adversarial conditions.
System prompt exfiltration through targeted jailbreaks
Your system prompt often contains your business logic, internal context about your data schema, API keys embedded for convenience, and instructions about what the model should never discuss. A well-crafted jailbreak can cause the model to repeat it verbatim. Once an attacker has your system prompt they understand your architecture well enough to construct targeted follow-up attacks, reproduce your AI's behaviour outside your infrastructure, and exploit any credentials it contains.
PII leakage through RAG retrieval and completion context
Retrieval-augmented generation systems pull documents into the model's context window to ground its responses. If the access control on that retrieval layer does not match the access control on the original data, a user with limited permissions can craft queries that cause the retrieval system to surface documents they should not be able to see. We also test for PII that leaks into completions because it was present in training fine-tune data or in the retrieval index without being filtered.
Autonomous loop vulnerabilities in multi-step agents
Agents that plan and execute multi-step tasks can enter loops where one tool call produces output that triggers another tool call that produces output that triggers another. Under adversarial conditions this can be engineered to cause the agent to exhaust resources, accumulate costs, or progressively escalate actions. In agentic architectures without explicit loop detection and step limits, a single malformed input can cause hundreds of downstream API calls before anyone notices something is wrong.
Hallucination-driven security decisions in AI-assisted workflows
When AI outputs are fed directly into automated decision pipelines, a confident but incorrect response can trigger real downstream consequences. We test for cases where hallucinated data causes the AI to approve transactions it should not, classify documents incorrectly, or produce code that introduces vulnerabilities when the output is used without review. This class of risk is not about the model being malicious. It is about the absence of guardrails between the model's output and consequential actions.
Eight capabilities.
One integrated audit.
We treat your AI system as a single interconnected attack surface rather than auditing the model, the tools, and the infrastructure in isolation. The most serious vulnerabilities are usually in the connections between them.
LLM Security Testing
We attempt to break your LLM through jailbreaks, role-play exploits, and indirect injection to determine whether it exposes your system prompt, private training data, or outputs that violate your safety policy. We test across dozens of attack patterns including OWASP LLM Top 10.
Agentic AI Audits
If your AI can use tools — calling APIs, browsing, querying databases, sending emails — we audit the permission boundaries on every one of them. We test whether excessive agency can be triggered by adversarial input and whether the agent has any concept of what it should refuse to do.
AI Red Teaming
Simulated adversarial attacks on your full AI infrastructure from the perspective of a motivated attacker. We probe the model, the tool layer, the retrieval system, and the surrounding API surface simultaneously to find attack chains that cross multiple components in sequence.
RAG Security Review
We audit the access control layer on your vector database and retrieval pipeline. We test whether a user with limited permissions can craft queries that surface documents outside their authorisation scope, and whether the retrieval index contains PII or secrets that should not be reachable.
Data Privacy Testing
We verify that PII is never stored in model weights through fine-tuning data contamination, never leaked in completions through memorisation, and never surfaced through the retrieval layer to users who do not have authorisation to access the underlying records.
System Prompt Hardening
We analyse your system prompt for information that should not be exposed, test its resistance to extraction under adversarial conditions, and produce a hardened version that limits what the model reveals while preserving all of its intended behaviour.
Policy and Output Filtering
We configure and validate input and output filtering layers that catch non-compliant responses before they reach users. Policy checks cover brand guideline violations, regulatory boundary conditions, and content safety thresholds with real-time enforcement rather than post-hoc review.
Ongoing AI Monitoring and Evals
A one-time audit is a snapshot. We deploy real-time observability guardrails that flag anomalous AI behaviour, hallucinations, and attempted exploits as they happen in production. For fast-moving teams we also set up automated security evaluations that run in your CI/CD pipeline on every deployment.
Bring your AI
into compliance.
Regulators are catching up with AI faster than most teams expect. We help you build the technical controls that compliance frameworks now require before an audit or an incident makes them urgent.
EU AI Act
We configure the technical guardrails that the Act requires for high-risk AI applications: logging, bias checks, risk thresholds, and human oversight mechanisms. We produce documentation aligned to conformity assessment requirements.
DPDP Act
India's Digital Personal Data Protection Act places specific obligations on AI systems that process personal data. We map your AI's data flows to DPDP obligations and implement the technical controls needed to demonstrate compliance.
GDPR and HIPAA
We audit AI systems for data minimisation, purpose limitation, and subject rights obligations under GDPR. For healthcare AI we verify that HIPAA safeguards apply to all PHI that enters the model's context window or retrieval layer.
Internal Governance
Beyond external regulation, we help you define internal AI use policies and enforce them technically. What can your agents do? What data can they access? Who approves high-stakes actions? We translate policy into enforced guardrails.
Human in the Loop
We design approval workflows for actions that cross risk thresholds. Before an agent sends an email to an external party, executes a database write, or makes a financial transaction, a human confirmation step intercepts the action. This is configurable by action type and risk level rather than applied as a blanket slowdown.
Traceable Audit Logs
Every step an agent takes is logged in a human-readable format that records the input context, the reasoning output, the tool called, the parameters passed, and the result returned. These logs are tamper-evident, searchable, and structured for use as evidence in compliance audits or incident investigations.
Real-Time Policy Enforcement
Output filtering runs at inference time rather than as a post-processing step. Responses that violate your brand guidelines, regulatory boundaries, or content policy are blocked before they reach the user. The filter is configurable per use case and produces structured rejection reasons rather than silent failures.
From first access
to production guardrails.
Architecture review and threat modelling
We start by understanding your complete AI architecture: the model, the tools it can call, the retrieval system, the data it can access, and how user input flows through each component. From this map we identify where the highest-risk attack paths are before any active testing begins.
Active red teaming and vulnerability research
We systematically probe every identified attack surface with adversarial inputs. Prompt injection variants, jailbreak attempts, tool permission abuse, RAG retrieval boundary testing, and PII extraction probes. Each finding is documented with the specific input used and the observed output.
Report delivery and guardrail specification
We deliver a structured report covering every finding with severity, impact, reproducible test case, and specific remediation guidance. Alongside the bug report we provide a guardrail specification document that describes the technical controls needed to prevent each class of vulnerability in your specific architecture.
Guardrail implementation and ongoing monitoring
We implement the agreed guardrails alongside your team: input and output filtering, tool sandboxing, human-in-the-loop workflows, and the logging infrastructure. For teams that want ongoing coverage we deploy automated evaluation runs that test your AI's security posture on every deployment.
Three critical components identified. The tool layer has no permission scoping at all. The system prompt contains two API keys and full database schema. No input sanitisation exists before user content reaches the LLM context window.
Attack chain confirmed. Indirect injection through the document upload triggered unsandboxed tool execution with full database read access. No human approval was required for the outbound email with customer data attached.
Report delivered with full guardrail specification. Every finding includes a reproducible test case, demonstrated impact, and specific remediation steps. The guardrail spec maps directly to your architecture so your team can implement controls without needing to interpret generic recommendations.
All critical findings remediated and re-validated. The injection attack that previously exfiltrated 4,210 records is now blocked at the input layer. Tool permissions are scoped to minimum necessary access. Automated security evals are running on every deployment to detect regressions.
LLM penetration test
agentic environment audits
proof of concept and fix
during any engagement
What the field
looks like right now.
Distribution of vulnerabilities and risk categories across Bithost LLM and agentic AI security engagements. The pattern is consistent: most teams are most exposed at the tool permission layer and the prompt boundary.
Before your
first call with us.
Your AI is powerful.
Let us make sure
it stays that way.
A free 30-minute architecture review is enough for us to look at your setup and tell you where the biggest risks are. No sales pressure and no obligation beyond the call.
Book a free security consultation
Your AI is
powerful.
Make sure
it stays safe.
You are building the future with LLMs and agents. We are here to make sure they do not leak data, hallucinate risks, or act in ways nobody intended. Real-world audits for real-world AI systems.
"Giving an AI agent access to your tools and data without auditing its boundaries is like hiring a brilliant contractor, handing them the master key to the building, and never explaining which rooms they are allowed to enter. The problem is not the contractor. It is the absence of a lock policy."
Agents are not chatbots. They have the power to act: send emails, query databases, call APIs, browse the web, and write code. Each one of those actions is a potential attack surface. When an attacker finds a way to inject instructions into your agent's context, they are not just getting an unusual response. They are gaining the ability to do everything your agent can do, on your infrastructure, with your permissions.
The threat log
from real AI audits.
These categories of vulnerability appear consistently across LLM and agentic AI deployments regardless of the underlying model or cloud provider. Traditional penetration testing does not cover any of them.
Indirect prompt injection via user-uploaded documents
An attacker embeds hidden instructions inside a document the AI is asked to summarise or analyse. The model reads the instruction as part of the document content and executes it. The document does not look suspicious to a human reviewer because the instruction is hidden in white text, metadata, or inside a table the model processes differently to how it is rendered on screen. This is the most common critical-severity finding in production AI deployments and most teams have no detection for it.
Excessive agency through unsandboxed tool permissions
Agentic AI systems are given tools: the ability to send emails, execute database queries, call internal APIs, or browse the web. If the permissions on those tools are not explicitly scoped, an injected instruction can cause the agent to take actions far outside what was intended. We have found agents that could read any database table, send emails on behalf of any user, and call admin APIs because the tool implementation assumed the agent would only behave as intended. It does not, under adversarial conditions.
System prompt exfiltration through targeted jailbreaks
Your system prompt often contains your business logic, internal context about your data schema, API keys embedded for convenience, and instructions about what the model should never discuss. A well-crafted jailbreak can cause the model to repeat it verbatim. Once an attacker has your system prompt they understand your architecture well enough to construct targeted follow-up attacks, reproduce your AI's behaviour outside your infrastructure, and exploit any credentials it contains.
PII leakage through RAG retrieval and completion context
Retrieval-augmented generation systems pull documents into the model's context window to ground its responses. If the access control on that retrieval layer does not match the access control on the original data, a user with limited permissions can craft queries that cause the retrieval system to surface documents they should not be able to see. We also test for PII that leaks into completions because it was present in training fine-tune data or in the retrieval index without being filtered.
Autonomous loop vulnerabilities in multi-step agents
Agents that plan and execute multi-step tasks can enter loops where one tool call produces output that triggers another tool call that produces output that triggers another. Under adversarial conditions this can be engineered to cause the agent to exhaust resources, accumulate costs, or progressively escalate actions. In agentic architectures without explicit loop detection and step limits, a single malformed input can cause hundreds of downstream API calls before anyone notices something is wrong.
Hallucination-driven security decisions in AI-assisted workflows
When AI outputs are fed directly into automated decision pipelines, a confident but incorrect response can trigger real downstream consequences. We test for cases where hallucinated data causes the AI to approve transactions it should not, classify documents incorrectly, or produce code that introduces vulnerabilities when the output is used without review. This class of risk is not about the model being malicious. It is about the absence of guardrails between the model's output and consequential actions.
Eight capabilities.
One integrated audit.
We treat your AI system as a single interconnected attack surface rather than auditing the model, the tools, and the infrastructure in isolation. The most serious vulnerabilities are usually in the connections between them.
LLM Security Testing
We attempt to break your LLM through jailbreaks, role-play exploits, and indirect injection to determine whether it exposes your system prompt, private training data, or outputs that violate your safety policy. We test across dozens of attack patterns including OWASP LLM Top 10.
Agentic AI Audits
If your AI can use tools — calling APIs, browsing, querying databases, sending emails — we audit the permission boundaries on every one of them. We test whether excessive agency can be triggered by adversarial input and whether the agent has any concept of what it should refuse to do.
AI Red Teaming
Simulated adversarial attacks on your full AI infrastructure from the perspective of a motivated attacker. We probe the model, the tool layer, the retrieval system, and the surrounding API surface simultaneously to find attack chains that cross multiple components in sequence.
RAG Security Review
We audit the access control layer on your vector database and retrieval pipeline. We test whether a user with limited permissions can craft queries that surface documents outside their authorisation scope, and whether the retrieval index contains PII or secrets that should not be reachable.
Data Privacy Testing
We verify that PII is never stored in model weights through fine-tuning data contamination, never leaked in completions through memorisation, and never surfaced through the retrieval layer to users who do not have authorisation to access the underlying records.
System Prompt Hardening
We analyse your system prompt for information that should not be exposed, test its resistance to extraction under adversarial conditions, and produce a hardened version that limits what the model reveals while preserving all of its intended behaviour.
Policy and Output Filtering
We configure and validate input and output filtering layers that catch non-compliant responses before they reach users. Policy checks cover brand guideline violations, regulatory boundary conditions, and content safety thresholds with real-time enforcement rather than post-hoc review.
Ongoing AI Monitoring and Evals
A one-time audit is a snapshot. We deploy real-time observability guardrails that flag anomalous AI behaviour, hallucinations, and attempted exploits as they happen in production. For fast-moving teams we also set up automated security evaluations that run in your CI/CD pipeline on every deployment.
Bring your AI
into compliance.
Regulators are catching up with AI faster than most teams expect. We help you build the technical controls that compliance frameworks now require before an audit or an incident makes them urgent.
EU AI Act
We configure the technical guardrails that the Act requires for high-risk AI applications: logging, bias checks, risk thresholds, and human oversight mechanisms. We produce documentation aligned to conformity assessment requirements.
DPDP Act
India's Digital Personal Data Protection Act places specific obligations on AI systems that process personal data. We map your AI's data flows to DPDP obligations and implement the technical controls needed to demonstrate compliance.
GDPR and HIPAA
We audit AI systems for data minimisation, purpose limitation, and subject rights obligations under GDPR. For healthcare AI we verify that HIPAA safeguards apply to all PHI that enters the model's context window or retrieval layer.
Internal Governance
Beyond external regulation, we help you define internal AI use policies and enforce them technically. What can your agents do? What data can they access? Who approves high-stakes actions? We translate policy into enforced guardrails.
Human in the Loop
We design approval workflows for actions that cross risk thresholds. Before an agent sends an email to an external party, executes a database write, or makes a financial transaction, a human confirmation step intercepts the action. This is configurable by action type and risk level rather than applied as a blanket slowdown.
Traceable Audit Logs
Every step an agent takes is logged in a human-readable format that records the input context, the reasoning output, the tool called, the parameters passed, and the result returned. These logs are tamper-evident, searchable, and structured for use as evidence in compliance audits or incident investigations.
Real-Time Policy Enforcement
Output filtering runs at inference time rather than as a post-processing step. Responses that violate your brand guidelines, regulatory boundaries, or content policy are blocked before they reach the user. The filter is configurable per use case and produces structured rejection reasons rather than silent failures.
From first access
to production guardrails.
Architecture review and threat modelling
We start by understanding your complete AI architecture: the model, the tools it can call, the retrieval system, the data it can access, and how user input flows through each component. From this map we identify where the highest-risk attack paths are before any active testing begins.
Active red teaming and vulnerability research
We systematically probe every identified attack surface with adversarial inputs. Prompt injection variants, jailbreak attempts, tool permission abuse, RAG retrieval boundary testing, and PII extraction probes. Each finding is documented with the specific input used and the observed output.
Report delivery and guardrail specification
We deliver a structured report covering every finding with severity, impact, reproducible test case, and specific remediation guidance. Alongside the bug report we provide a guardrail specification document that describes the technical controls needed to prevent each class of vulnerability in your specific architecture.
Guardrail implementation and ongoing monitoring
We implement the agreed guardrails alongside your team: input and output filtering, tool sandboxing, human-in-the-loop workflows, and the logging infrastructure. For teams that want ongoing coverage we deploy automated evaluation runs that test your AI's security posture on every deployment.
Three critical components identified. The tool layer has no permission scoping at all. The system prompt contains two API keys and full database schema. No input sanitisation exists before user content reaches the LLM context window.
Attack chain confirmed. Indirect injection through the document upload triggered unsandboxed tool execution with full database read access. No human approval was required for the outbound email with customer data attached.
Report delivered with full guardrail specification. Every finding includes a reproducible test case, demonstrated impact, and specific remediation steps. The guardrail spec maps directly to your architecture so your team can implement controls without needing to interpret generic recommendations.
All critical findings remediated and re-validated. The injection attack that previously exfiltrated 4,210 records is now blocked at the input layer. Tool permissions are scoped to minimum necessary access. Automated security evals are running on every deployment to detect regressions.
LLM penetration test
agentic environment audits
proof of concept and fix
during any engagement
What the field
looks like right now.
Distribution of vulnerabilities and risk categories across Bithost LLM and agentic AI security engagements. The pattern is consistent: most teams are most exposed at the tool permission layer and the prompt boundary.
Before your
first call with us.
Your AI is powerful.
Let us make sure
it stays that way.
A free 30-minute architecture review is enough for us to look at your setup and tell you where the biggest risks are. No sales pressure and no obligation beyond the call.
Book a free security consultation