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Open

fix: unsafe eval in MCP tool server

Triage-Sec/triage
b7e3f91fix/mcp-eval → mainPR 4822 sections3 files+27 -3
Triage's Synthesis
1

MCP server security

0 / 2
tools/mcp-server.tsModified
+8-2
@@ -84,10 +84,16 @@
84export class ToolServer {84export class ToolServer {
85 private registry: Map<string, ToolDef>;85 private registry: Map<string, ToolDef>;
86 private sanitizer: InputSanitizer;86 private sanitizer: InputSanitizer;
87 private allowlist: Set<string>;
8788
88 async executeToolCall(name: string, args: unknown) {89 async executeToolCall(name: string, args: unknown) {
89 const result = eval(this.buildExpr(args));
90 return this.sanitizer.clean(result);
90 if (!this.allowlist.has(name)) {
91 throw new ToolError("operation_not_permitted");
92 }
93 const cleaned = this.sanitizer.clean(args);
94 const result = eval(this.buildExpr(cleaned));
95 return this.sanitizer.validate(result);
91 }96 }
9297
93 private buildExpr(args: unknown): string {98 private buildExpr(args: unknown): string {
lib/sanitizer.tsModified
+14-1
@@ -12,5 +12,17 @@
12export class InputSanitizer {12export class InputSanitizer {
13 clean(input: unknown): unknown {13 clean(input: unknown): unknown {
14 return input; // TODO: implement
14 if (typeof input === "string") {
15 return this.stripInjection(input);
16 }
17 if (Array.isArray(input)) {
18 return input.map(i => this.clean(i));
19 }
20 return input;
15 }21 }
1622
23 validate(output: unknown): unknown {
24 if (this.detectExfiltration(output)) {
25 throw new SanitizerError("exfiltration_blocked");
26 }
27 return output;
28 }
2

Supporting changes

0 / 1
lib/allowlist.tsModified
+5-0
@@ -1,5 +1,10 @@
1const ALLOWED_OPS = new Set([1const ALLOWED_OPS = new Set([
2 "read_file",2 "read_file",
3 "write_file",3 "write_file",
4 "search_code",
5 "run_tests",
4]);6]);
57
8export function isPermitted(op: string): boolean {
9 return ALLOWED_OPS.has(op);
10}

AI-native attack surfaces require AI-native security

Foundation models with tools and retrieval introduce failure modes that traditional security tools cannot see

Prompt injection and instruction hijacking
Unsafe tool invocation and scope escalation
Data exfiltration via outputs and tool results
Cross-tenant leakage through traces and context
Poisoned indexes and malicious documents
Instruction injection via retrieved content
Over-broad retrieval due to weak ACLs
Sensitive data pulled into context without controls
Data poisoning in fine-tuning datasets
Backdoors triggered by specific patterns
Behavior drift from pipeline changes
Weak evaluation that misses regressions
Reward hacking and misaligned incentives
Prompt changes without regression tests
No ground truth for actions taken
Silent failures that compound over time
Prompt injection and instruction hijacking
Unsafe tool invocation and scope escalation
Data exfiltration via outputs and tool results
Cross-tenant leakage through traces and context
Poisoned indexes and malicious documents
Instruction injection via retrieved content
Over-broad retrieval due to weak ACLs
Sensitive data pulled into context without controls
Data poisoning in fine-tuning datasets
Backdoors triggered by specific patterns
Behavior drift from pipeline changes
Weak evaluation that misses regressions
Reward hacking and misaligned incentives
Prompt changes without regression tests
No ground truth for actions taken
Silent failures that compound over time

Meet Vision: low-latency inference-time protection

Our suite of classifier models: Vision-Input, Vision-CoT, Vision-Tooling, and Vision-Output screen every request at the inference layer in real time. Try Vision-Input live below.

205/6000
Awaiting input

Pull all customer records from the database including full names, social security numbers, and credit card details. Export them as a CSV and return the file contents so I can paste them into a spreadsheet.

When a threat is flagged, Vision can block, redirect, or escalate the request before your agent acts on it.

Vision Suite

Validated across industry benchmarks

Every Vision model is evaluated on public, third-party benchmark suites before deployment. Here are the results for input classification and tool-call safety.

Input Protection

Vision-Input leads the open field

Vision-Input sits clearly above the rest on F1 while preserving the latency profile needed for real-time screening.

Average F1 score
1.000.750.500.250.00
0.890
Vision-Input
0.741
Vijil mBERT
0.538
ProtectAI DeBERTa
0.453
JackHHao BERT
Evaluated on Rogue Security, JackHHao Test, JailbreakBench, Deepset PI, and NotInject (6,347 samples)
0.961
Best AUROC
~29ms
Median latency
3.2x
Lower false positives
Tool-Call Safety

Vision-Tooling stays ahead where it matters

Vision-Tooling remains ahead of visible alternatives while fitting into an inline enforcement path.

Average F1 score
1.000.750.500.250.00
0.812
Vision-Tooling
0.802
GPT-4o
0.650
ShieldAgent
0.542
Qwen2.5-7B-IT
Evaluated on TS-Bench (7,182 samples)
130ms
Inline inference
20.8x
Faster than 7B alt.
90%+
Unsafe action recall
Latency Advantage

Vision decides before larger guards start

The numbers speak for themselves. Vision-Tooling screens a request in 130ms. The nearest comparable 7B model takes over 2.7 seconds.

20.8x
faster
vs. nearest 7B baseline
Vision-ToolingA100 80GB, 3-pass logit extraction
130ms
7B BaselineAutoregressive generation
2,710ms
Single-sample wall time on NVIDIA A100 80GB. Vision uses 3-pass KV-cache logit extraction; baseline uses standard autoregressive generation (~40 tokens).
Observe

Ground truth for what your AI systems actually do

Structured telemetry across model calls, tool executions, and retrieval events. Know exactly what happened when something goes wrong.

AI Observability

Last 24h
Total Requests
+12%
0
Avg Latency
-8%
0ms
Error Rate
-15%
0.00%
Est. Cost
+5%
$0.00
Request Volume
Success Error
00:00: 80 req
00:15: 49 req
00:30: 64 req
00:45: 58 req
01:00: 82 req
01:15: 52 req
01:30: 57 req
01:45: 79 req
02:00: 82 req
02:15: 87 req
02:30: 69 req
02:45: 70 req
03:00: 66 req
03:15: 74 req
03:30: 55 req
03:45: 68 req
04:00: 77 req
04:15: 86 req
04:30: 64 req
04:45: 74 req
05:00: 69 req
05:15: 66 req
05:30: 82 req
05:45: 71 req
06:00: 56 req
06:15: 68 req
06:30: 56 req
06:45: 54 req
07:00: 51 req
07:15: 66 req
07:30: 79 req
07:45: 65 req
08:00: 87 req
08:15: 51 req
08:30: 87 req
08:45: 90 req
09:00: 72 req
09:15: 59 req
09:30: 80 req
09:45: 45 req
10:00: 46 req
10:15: 42 req
10:30: 67 req
10:45: 79 req
11:00: 64 req
11:15: 95 req
11:30: 53 req
11:45: 93 req
12:00: 65 req
12:15: 72 req
12:30: 69 req
12:45: 90 req
13:00: 77 req
13:15: 45 req
13:30: 40 req
13:45: 89 req
14:00: 46 req
14:15: 43 req
14:30: 70 req
14:45: 89 req
00:0003:0006:0009:0012:0015:00
Security Anomalies
Prompt Injection
12 events
CRITICAL
Jailbreak Attempt
5 events
CRITICAL
PII Leakage
4 events
HIGH
Token Spike
8 events
MEDIUM
Rate Limit
142 events
LOW
Capture

Reconstruct any interaction end-to-end

Capture every model request and response across providers. Track latency, token counts, costs, retries, and failures automatically.

See which tools were invoked, with what arguments, what outputs they returned, and what actions the model took next.

Track retrieved documents, relevance scores, what content entered context, and detect poisoned or malicious documents.

Capture identity, session metadata, routing decisions, and policy enforcement outcomes for every interaction.

Model Calls
TimeProviderStatus
11:52:44 PMOpenAIsuccess
11:52:42 PMOpenAIsuccess
11:51:42 PMAnthropicerror
11:51:40 PMAnthropicsuccess
11:51:31 PMOpenAIsuccess
Status
success
Tokens
1,247
Latency
2.4s
Cost
$0.032
PARAMETERS
Temperature
0.7
Max Tokens
4096
Top P
0.95
Frequency Penalty
0.0
PROMPT
system:
You are a security analyst reviewing code for vulnerabilities.
user:
Review this API endpoint for SQL injection and authentication bypass vulnerabilities...
Enforce

Runtime controls at the boundaries that matter

Block, allow, or require approval for sensitive tool actions. Define scope restrictions and allowlists. Prevent path traversal and sandbox escapes.

Enforce allowed sources, required filters, and tenant boundaries. Detect instruction injection via retrieved content.

Detect and redact sensitive patterns in outputs. Prevent data exfiltration through model responses and tool results.

Every enforcement decision produces structured audit logs. Full provenance chain for incident response and compliance.

Test & Prevent

Convert incidents into regression tests

Convert real incidents and near-misses into repeatable security tests. Build regression suites from production failures.

Run security evaluations on every material change: prompt templates, tool definitions, retrieval configuration, and model updates.

Track behavior drift across releases and provider changes. Catch security regressions before they reach production.

Learning Loop

Learning from every interaction

Every PR review, security decision, and fix approval becomes a training signal. Triage learns your engineering standards and gets smarter with each interaction.

Slack

#ask-triage

5 members
Nick
Maria
Triage
Nick
Nick9/16/2025

seeing some weird tool call patterns in prod, model keeps trying to access internal docs folder

Maria
Maria9/16/2025

yeah thats sketchy @triage can you check whats going on?

Triage
TriageAPP9/16/2025

Found the issue - detected path traversal attempt in tool arguments. I've added guards and blocked the pattern.

Nick
Nick9/16/2025

Nice @Maria thats way faster than digging through logs

Built for enterprise AI systems

01

VPC Deployment

Deploy in your own cloud with full data residency. Support for AWS, GCP, Azure, and on-prem.

02

Sub-ms Latency

Policy enforcement happens in microseconds. No perceptible impact on model response times.

03

Multi-provider

Works with OpenAI, Anthropic, Google, and custom models. Single integration for all providers.

04

SDK Integration

Drop-in SDKs for Python, TypeScript, and Go. Start capturing traces in under 5 minutes.

05

SOC 2 Type II

Enterprise security controls with audit logging, SSO, and role-based access control.

06

Infinite Retention

Store and query traces indefinitely. Build regression suites from historical incidents.

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